# Model of models - Part 1

###### Abstract

This paper proposes a new cognitive model, acting as the main component of an
AGI agent. The model is introduced in its mature intelligence state, and as an
extension of previous models, DENN, and especially AKREM, by including
operational models (frames/classes) and will. This model’s core assumption is
that cognition is about operating on accumulated knowledge, with the guidance
of an appropriate will. Also, we assume that the actions, part of knowledge,
are learning to be aligned with will, during the evolution phase that precedes
the mature intelligence state.

In addition, this model is mainly based on the duality principle in every
known intelligent aspect, such as exhibiting both top-down and bottom-up model
learning, generalization verse specialization, and more. Furthermore, a
holistic approach is advocated for AGI designing and cognition under
constraints or efficiency is proposed, in the form of reusability and
simplicity. Finally, reaching this mature state is described via a cognitive
evolution from infancy to adulthood, utilizing a consolidation principle.

The final product of this cognitive model is a dynamic operational memory of
models and instances.

Finally, some examples are presented, and some preliminary ideas for the
evolution phase to reach the mature state.

Shimon Komarovsky , Technion, Haifa, Israel

##  1 Introduction

Our consistent goal is to construct a basic realistic model for AGI
(Artificial General Intelligence). To accomplish that, there exist many ways
to approach the AGI problem, e.g. via emulation. Although, a human brain
emulation (copying the brain into digital form) is a good idea, there are some
deficiencies to this approach:

  1. 1.

the brain is a very complex organ, especially when it is accessed at its
mature state.

  2. 2.

there is no direct access to the human experience (consciousness) with
correlation to the brain activity (or neural processing). It is mostly
performed in a very indirect process, like questioning a human participator.
Also it is done in a very coarse resolution of the brain (large clusters of
neurons).

  3. 3.

it is difficult to model the brain, since it works throughout the whole
network. Hence, simple models cannot be extracted out of it by local
isolation. See more in https://medium.com/nontrivial/neuro-
nonsense-2acc209d42c3

Hence, here the AGI problem is tackled from a human design direction instead,
though neuroscience can act as a source for ideas and inspiration [Zhao et
al., 2023]. This design is a gradual process with many versions along the way.
Therefore, this paper presents MOM (Model Of Models), the next version of
AKREM (Associative Knowledge Representation). See its short version
[Komarovsky, 2022b] or its full version [Komarovsky, 2022c].

AKREM is a mature-state knowledge representation model, based mainly on the
assumption that communication is about encoding the sender’s will into a
sequence of words (a message), and then decoding it by the recipient. The
model proposes a representation of any message, in a hierarchical form based
on grouping, by generating some essence in a given level, from details in the
lower level. The lowest level details are founded upon some DNN (Deep Neural
Network), generating the basic concepts and actions (from which the details
are made of) from unstructured input.

AKREM is also backed by neuroscience, such as the idea of memorizing
techniques such as the Memory Palace, which turns random objects into
memorable ones by converting them into some consistent sequence or tale. Also,
[Hahamy et al., 2023] supports the idea that story or any message is
partitioned into micro-events, which are also inter-connected to allow
comprehending current part of the story based on previous parts of it (a
phenomena called ”replay”).

Finally, while the will concept exists in AKREM, it will be expanded upon in
this paper.

The will is very fundamental to our intelligence. It is the source of
everything. It dictates our actions, our interpretation of reality, thus
affect the meaning of things to us. Nevertheless, it is almost not mentioned
in AI literature. Our main cognitive will is to comprehend the reality, in
order to manifest other types of will in it. More generally, it is expressed
via problem-solving state of mind, in which we react in reality. Nevertheless,
will can be also affected by reality.

Furthermore, we assume the will is a field, similar to the ”will force”
phrase, i.e. it is represented as vector in some state space. Then, in order
to affect reality, this will has to create some connection to knowledge. We
assume that this connection is learned through experience, and it is
manifested by alignment with the actions operating on objects that comprise
the knowledge. This way, whenever we have a will, the appropriate actions are
triggered, similarly to when we want to raise our arm - the appropriate
physical actions operate.

However will is an intriguing phenomenon. It can be realized via laws of
physics, or derived from emotions, or even derived deeply from high (moral)
values, such as justice. This makes the full knowledge model of anything, as
observed from outside, very stochastic, due to these hidden factors.

Following this, new additions to the presented model, including new
associations, operationability, modeling, consolidation, and reusability, are
introduced. First, while AKREM assumes that the learned elements are either
objects or static actions (verbs), new associations are introduced: object’s
attributes and relations. Next, these connections are all static
representations of knowledge, i.e., the hierarchies cannot be changed.
Therefore, operationability introduces a new type of association to objects:
actions that act upon them, thus producing new knowledge elements. This makes
the connections in AKREM’s hierarchies dynamic, hence it allows the freedom to
update and create new hierarchies. Next, modeling introduces some basic
cognitive operations, e.g. abstraction and grouping111Philosophically, these
operations drive us to theory of everything, and religiously to the notion of
God.. Both gather many details into fewer. Grouping specifically is about
connecting elements via some common property. It could be for example a
chronology in a plot or other common properties/actions grouped into classes.
Finally, consolidation is a process in time, that collapses a huge amount of
possibilities into small set of patterns, of any kind.

All the operations above are considered to be bidirectional, i.e. everything
lies in some range between extremes, i.e. everything has its inverse
(dualism)222Can also be regarded as symmetries or invariants. In grouping, it
is from the whole to its parts and vice versa, and in abstraction, it is from
instances to classes and vice versa. Consolidation is an operation in time,
creating models and memory, while its inverse is forgetting, which is also an
operation in time. Will also lies in the dichotomy of determinism and
randomness.

Moreover, if in the early epoch of AI, symbolic reasoning was dominant, and
nowadays connectionism dominates, then we come to a new era [Sheth and
Thirunarayan, 2021], where we should combine and include many conflict
perspectives, in cooperation and competition. Our holistic perspective embrace
this duality and other dualities also [Reggia, 2013]. An exhaustive but not
complete list is presented:

  * •

top-down and bottom-up model learning,

  * •

consolidation verse forgetfulness,

  * •

generalization verse specialization,

  * •

grouping and dismantling,

  * •

determinism and randomness,

  * •

problem-solving verse designing perspectives,

  * •

connectionism verse symbolism,

  * •

soul and brain (idealism),

  * •

convergent verse divergent thinking,

  * •

object-oriented verse functional programming paradigms,

  * •

past verse present perceptions,

  * •

induction verse deduction,

  * •

recognition verse creativity,

  * •

serial thinking verse parallel thinking (as described be de Bono, e.g. in De
Bono [2016]),

  * •

left-brain verse right-brain theories,

  * •

neuron excitation verse inhibition,

  * •

exploration verse exploitation, and more.

The product of these cognitive operations and dualities is a dynamic memory of
models, formed as a semantic network of elements. It is dynamic by being
evolving through time, by including spatio-temporal modeling, and by being
operational, i.e. by possessing the possibility to update the
models.Additionally, it encourages a holistic approach for AGI designing: one
simple system for multiple functions, such as short-term memory (STM) and
long-term memory (LTM), problem-solving, communication, learning, and any
cognitive function. This approach is also encouraged in neuroscience, i.e.,
though the brain seems complex, but its basic mechanism is simple
[Mountcastle, 1997]. This approach is especially needed when resources are
limited. By using this approach, our MOM puts everything in one place, instead
of common cognitive architectures approach to separate into modules. Here
there is a dynamic memory, which is also used for thinking, planning,
imagining, and more. While the Working Memory (WM) is simply loading old
memories of LTM ”into the surface”.

Following this dynamic representation of knowledge - its evolution is
presented. That is, operational modeling presents AGI agent’s intelligence in
its mature state, which is the state of how its knowledge should be
represented. However, to accomplish this state, a cognitive evolution over
time is required, which utilizes the consolidation principle. Along with this
primary consolidation process, that act in many different things, there are
also some fundamental learning approaches. These approaches are the learning
from examples and the learning from logic, use consolidation as a tool, to
reach the desired mature state we present in this paper.

The two parts of the MOM cognitive model are illustrated in Fig. 1. They
represent the duality above, especially our Neuro-Symbolic approach.

Figure 1: MOM two parts.

MOM in theory includes objects, relations, actions, abstractions, etc – mostly
logic and classic AI. On the other hand, MOM in practice based mostly on DL,
and includes concepts/principles like consolidation, attention, splitting &
merging as in DENN, and more.

Eventually, this framework has different approach to learning compared to
usual DL, such as Large-Language-Models (LLMs), in the journey to reach AGI.
DL based on learn-everything-at-once kind of approach, or batch learning. In
this approach the expert AI agent supposed to learn everything mixed up with
different topics and complexities. Hence, the way to reach AGI, is by larger
models and data. Conversely, we support incremental curriculum type of
learning as in humans. The learning is built in layers or stages. When the
current layer is established, then and only then that we can proceed to a new
layer on top of the previous layers. Usually it is done by extending analogies
(distant connections), which can be regarded as breadth extension, and
abstraction, which can be regarded as extension in height.

Finally, after establishing a firm theoretical framework of MOM, a proposed
implementation is presented. That is, the implementation of the evolution
phase that strive to reach the mature intelligence state. It is based on the
principle of model separation/isolation, then the use of attention
splittability feature to realize this, then the use of multi-version to allow
flexibility in learning, finishing with program-search to model methods of
consolidated classes/objects.

###  1.1 Motivation and Problem definition

####  1.1.1 General motivation

The general motivation is to reach AGI. We consider LLMs as the first step
towards this goal. However, the deficiencies of these LLMs is what inspire us
to succeed where they fail. For example, ChatGPT performs quite well in many
tasks, hence GPT can be regarded as general purpose AI model, and can
generalize to new tasks. However, it hallucinates with confidence, and do not
perform well in reasoning tasks, that require logic and step-by-step thinking.
One of the reasons for this, is that masking or next word prediction is not
enough as a prior knowledge, to align with humans, even though some studies in
neuroscience showed correlation of brain activity and LLMs.

Also LLMs do not have a world model (unlike JEPA [Dawid and LeCun, 2023]).
They simply mimicking text or images (like Midjourney)
<EMAIL_ADDRESS>frontier-in-our-path-to-agi-is-here-ecab17042d1e.

Many papers have tried to improve reasoning. They used many methods, e.g.
Chain-of-thought (CoT) prompting, self-consistency, role prompting, voting by
agreement, Tree-of-Thoughts (ToT), and other prompting techniques. However,
these do not show ultimate solution. Another example, in [Ling et al., 2023],
they perform CoT but with accompanying an evaluation for each step.
Specifically, perform a deductive reasoning through evaluating each step. In
deductive reasoning, a (premise, conclusion) pair is given, and the goal is to
determine whether the conclusion follows from the premises. This method
demonstrates also the need in a much stronger prior, since the proposed method
searches for “fixes” to the CoT method, though the CoT method in LLMs is
fundamentally wrong approach to model reasoning.

A recent paper by Google Brain Schuurmans [2023] proves how adding a memory to
LLMs can produce Turing complete machine, i.e. simulate any algorithm. This
demonstrates that memory is definitely part of the needed prior, but it is not
all of it.

In our opinion, this prior should integrate different brain’s cognitive
capabilities/functions. Such a prior would allow the agent to learn and act
more rationally, thus produce the most valuable trait, that’s missing in
today’s (mostly black-box) AI: explainability (through external communication
with it. Not by analyzing its internal model). Hence, a more promising
approach we advocate for and try to apply, is the hybrid approach of Neuro-
Symbolic AI. It is a sub-field of AI, where the strengths of the two
disciplines are combined to overcome their weaknesses.

####  1.1.2 Specific motivation

This paper is the continuation of the previous paper’s model AKREM. The
context of the previous and the current paper is the same: assuming some
module that converts sub-symbolic data to symbolic one and vice versa. In
AKREM, it is a temporal hierarchical DNN, that processes the incoming data
from senses via different temporal rates, where the slowest processing layers
suppose to deal with thinking, then finally produces the appropriate output.

In this paper however, this module is more vague. On the one hand it can be
part of the whole system proposed in the paper: sub-symbolic to symbolic and
vice versa can use the same mechanism of learning as described in Section 7.2.
On the other hand, we can assume an external module similar to AKREM settings,
where some DNN, either flat or also temporal hierarchical, is responsible for
some fundamental processing of incoming and outcoming raw data. During this
processing neurons are triggered in the DNN. These neurons stimulate classes
of objects and actions, representing agent’s knowledge map. This is where a
transition from connectionist AI to classic/logic AI occurs.

Our main goal in this paper is to concentrate on the logical part, or the
knowledge map, to model the thinking/cognitive processes, as it is in our
opinion the bottleneck for AGI, or the more urgent issue in AI nowadays, such
as it appears in the problems revealed in LLMs.

This modeling, MOM, presented as the mature state of an intelligent agent, is
described in details in Sections 4, 5, 6.

Of course, this model is only the ideal intelligence state, hence how to reach
this state is discussed in Section 7.1.

Additionally, we figured out that thinking is very dependent upon will, either
the will encoded in the perceived message or the agent’s own will. This topic
is elaborated in Section 3.

Finally, we define thought as operating in the knowledge map, e.g. by
inferring class features/attributes, or by performing actions on objects, or
by overall supervising the thought process.

###  1.2 Contribution

The contribution of this paper is expressed within two comparisons: the
comparison of the proposed model to its previous versions, see Section 9.2,
and the comparison to other cognitive architectures, see Section 9.1.

##  2 Timeline

The following Sections are ordered like this:

  * •

Section 3 starts from some terminology, then discuss will as it was presented
in previous model: AKREM, then continues with will expression in a constrained
environment, especially in problem solving and design, then presents will as a
field and its consequences/implications, and then finishes with different
levels or types of will.

  * •

Section 4 describes briefly the new Associations added to our current model.

  * •

Section 5 is about operationability and modeling, two extensions of the
previous AKREM model. First it describes them both as simple classes/frames,
then details about each of them in our framework. Finally, few definitions are
introduced and then will derivation is discussed.

  * •

Section 6 is engaging in further details about MOM’s knowledge structure, and
then displaying several examples of MOM model.

  * •

Section 7 discusses temporal processes of consolidation in its multi-faced
forms, reusability principle as an additional form of consolidation, and
finishes with learning approaches.

  * •

Section 8 proposes one possible implementation of ideas presented in previous
chapters. It is founded upon the model separation capability, then the
utilization of attention mechanism to realize this capability. Then the multi
version for flexibility is used, and finally the program search for the
actions is discussed.

  * •

Section 9 is reviewing relevant literature in different topics, e.g. Neuro-
symbolic AI, cognitive architectures, and hierarchical planning and AI.
Finally it is comparing previous models with the current proposed MOM model.

  * •

Section 10 is a summary of the paper including some key takeaways, and later
discusses future work.

  * •

Finally, Appendix A includes: discussion of advantages for adopting a hybrid
approach for AGI; how will can be learned; existing knowledge representation
compared to MOM; the issue of Bias in any intelligence; a the role of language
in the AGI background; DNN’s equivalence to programming languages; general
evolution in AKREM; the aspects of Logic and Creativity in MOM; and AGI
additional characteristics.

Note that the paper is not so rigorous as any serious academic paper. It is
mostly philosophic, since this theory is foremost presented for the sake of
acting as a starting point for discussion and adaptations. It is not that
important to define every detail in the theory just yet. Nevertheless, along
the paper few definitions are spread, to ease on the reader, i.e. to make
things more clear and less ambiguous.

Either way, the reader should come open-minded, since this is not technical
paper. We invite and welcome notes, suggestions, reviews, corrections, and any
type of comments and discussion.

##  3 Will

In this section, the importance of will as an essential element in human
intelligence333One clue for this found in https://theconversation.com/great-
mysteries-of-physics-5-will-we-ever-have-a-fundamental-theory-of-life-and-
consciousness-203127, where they try to characterize how the living different
from non-living structures. They show that living structures for some reason
can defy entropy and can produce even more complex structures through
evolution, while non-living cannot. This may indicate the external factor of
will is what can cause this difference. is elaborated upon, starting from some
general motivation, then continuing from the previously presented model,
AKREM. The detailed discussion about how will is expressed in a constrained
environment is performed, via problem-solving setting. Finally, some basic
types of will are presented.

We first start with some terminology, so that the reader would have clear
context of the discussed in the rest of this chapter and other chapters.

###  3.1 Terminology

First we collect the relevant interpretations from the dictionary:

Will = power of the mind in choosing/controlling one’s own actions/emotions.

Goal = the result or achievement toward which effort is directed.
Purpose = the reason for which something is done, exists, created, made, used,
etc; something set up as an object or end to be attained; a subject under
discussion or an action in course of execution; an intended or desired result;
end; aim; goal.
Objective = something that one’s efforts or actions are intended to attain or
accomplish. Intention = an act or instance of determining mentally upon some
action or result; a determination to act in a certain way; the end or object
intended; meaning or significance; purpose.

Similarly, our definition of will is the source of actions. We refer purpose
as a goal. However, its other interpretations such as a reason or a meaning of
something and an action in course of execution are related. Since it is like
describing the how it got to be, or the will that derive it, or the will in
the process.

And taken from social studies [Marco, 1996, Gollwitzer, 1993, Reker et al.,
1987]:

Will = refers to an individual’s mental faculty or capacity to make choices
and decisions. It involves the conscious and deliberate exercise of one’s
volition or intention in order to initiate or refrain from certain actions.
Will is often associated with personal agency and the ability to exert control
over one’s behavior, thoughts, and emotions.
Goal = a desired outcome or result that an individual or a group of
individuals strive to achieve. It represents a specific target or destination
towards which efforts are directed. Goals can be short-term or long-term and
can be related to various aspects of life, such as personal development,
career aspirations, relationships, health, or academic achievements. They
provide a sense of direction and purpose, guiding individuals’ actions and
decisions.
Purpose = a sense of meaning or significance in one’s life. It involves having
a clear understanding of one’s values, passions, and overarching aims. Purpose
goes beyond specific goals or achievements and often involves a deeper
reflection on one’s identity, values, and contribution to the world. It
provides a sense of fulfillment and a motivation to engage in activities that
align with one’s purpose.
Objective = a specific, measurable, and time-bound step or milestone that is
designed to contribute to the attainment of a larger goal. Objectives are
concrete and quantifiable, allowing individuals or organizations to assess
progress and success. They serve as the building blocks that help individuals
or groups move closer to their overall goals.
Intention = a conscious and deliberate mental state characterized by a planned
or anticipated course of action. It involves the formation of a specific
purpose or objective in one’s mind and a commitment to act in accordance with
that purpose. Intentions can range from simple everyday actions to more
complex and long-term endeavors. They play a crucial role in guiding behavior
and directing efforts towards the achievement of desired outcomes.

Interestingly, these definitions are quite different from the dictionary’s
ones. Here they are more human-inclined or social, while the dictionary is
more general.

###  3.2 Motivation

One aspect of will, is that it determines our behavior, or more precisely -
our actions. Will derives or creates somehow a set of actions, i.e. like a
plan, for example a will causes for constructing an algorithm such as search
in a knowledge base or in an AGI/brain network. Inversely, will can be
inferred from an action or from a sequence of actions. This process of
producing actions and retrieving their original will is expressed in the
sequence encoding and decoding in AKREM, respectively. As seen in Fig. 2, will
or purpose is expressed both in single action (individuality), and in a
sequence of actions or a trajectory (grouping). More generally, it is in the
different levels of AKREM.

(a) Single action, e.g. in lower level

(b) Group of actions, e.g. in higher level

Figure 2: Actions representing a meaning/purpose

Another aspect of will, is its affect on our knowledge. Philosophically, will
+ objective data produce meaning and bias [Nietzsche, 1974], or more generally
perception and interpretation. Or:

will + objective data = subjective data. (1)

In other words, meaning is not an objective concept in non-human reality, but
rather it is ”tailored” specifically to humans, or more accurately it is a
function of will - or derived from it. I.e. it is related to the function that
the concept has to the human. For example, ”cat” perceived as a pet while wild
cat excluded from this concept since it is not under human control. Similarly,
any tool like a hammer or a screwdriver has a meaning as a function for
fulfilling some will, and not just as a particular shape and matter
properties. Along with this, it is natural that meaning would have a bias.
Hence, the bias and fairness which researchers battle with, is impossible to
overcome, since there will always be some bias, i.e. some preferred opinions
over others, again - as a function of the will. Best thing we can do is
externally change the will, which will yield different outcomes.

Moreover, meaning is the will’s manifestation, or its destination. It is the
end-product of will, like role in RRG model Van Valin [1995], or the
target/objective in optimization (the destination of some process) Lewis et
al. [2012]. Hence, meaning imply some designer’s (accomplished) will, e.g. a
chair is designed for sitting (and this is its meaning). See meaning as the
end-product of a message in a conversation in Fig 3(b).

Additionally, will is an important element in human intelligence, that is
almost not mentioned in the modern AI literature. It is expressed in today’s
machines, via the requirement from the designer to express his will in a very
limited way. For example, in control systems or in software - everything must
be designed in a very clear and accurate manner. It is expressed in the form
of an objective function in control systems or in AI.

In Natural Language Processing (NLP) will exists in its highest hierarchy, in
speech acts, above descriptive-only logical forms. Speech acts contains the
will in the message and not only its informative part, which forces the
receiver to react accordingly (to a request/question/claim/promise, etc).

Deep Learning (DL) tries to override inclusion of explicit will by supplying
examples to express will. It is hidden in the need for big data and attention
in DL models, disguised as an implicit will that determines what should be the
correct output. It is also hidden in the curiosity-driven Reinforcement
Learning (RL) models, where curiosity is only one specific type of will.
Instead, will should be addressed directly and explicitly in AI.

One intuitive way to insert will is externally, in a specific input channel
only designed for that. This is the explicit way. Complementary or as an
alternative, the communication channel can be used, accompanying with its
recent context, to infer the will from the message that an AGI agent receives.
This is the implicit way. It is practiced via prompting in language models
(LMs) and it is supported in AKREM and in MOM.

###  3.3 Extending will beyond AKREM

Will in AKREM is represented in the levels of any specific hierarchy. Starting
from the most detailed aspects of will, at the lowest level, and finishing at
the most abstract will or its essence, at the top. The top level represents
some kind of experience uniqueness to differentiate it from other memories
that use the same low-level structures.

This hierarchical will is especially demonstrated in a constrained
environment, as our reality, for topics like problem-solving and
communication. In problem-solving, the main will produces sub-wills in lower
levels, till it reaches the final solution at the bottom level (Fig 3(a)). The
final result is a plan or a sequence of actions. See more in Section 3.4.
Similarly, in communication, the sender encodes/converts his will into a
sequence of actions (in a language form), while the recipient on the other
side decodes the intention/will from this sequence (Fig 3(b)). In both cases,
evaluation is necessary, hence this top-down process is cyclic and non-linear.

(a) 3-level problem-solving in state space

(b) Communication model

Figure 3: Two cases of will in a constraint environment

All the above are specific cases of will, but there is also a more general
will. As recipients of reality, our main cognitive will is to find the most
appropriate/simple model to fit all the pieces/details in the right place or
to make the most sense of them444Making sense is also important for
explainability, which can be provided via storytelling modeling. Hence,
explainability should be generative and flexible, in its most general
conception. In other words, it is a problem-solving task, yielding a sequence
of words.. This is similar to decoding a will from a message/mystery/riddle,
and it can be rephrased as a general problem-solving task to comprehend
reality [Black and Bower, 1980]. First, it is done internally, by reorganizing
our models (mostly during a sleep phase), and later it is done externally, in
any kind of problem-solving, or in understanding a
message/story/riddle/situation/phenomena. Moreover, it is claimed in
psychology (https://www.psychologytoday.com/us/blog/the-resilient-
brain/202302/the-many-mental-benefits-of-decluttering): ”Not only is it hard
to physically function in a cluttered space, but clutter bombards the mind
with excessive stimuli. Addressing the heaps of paperwork, laundry, and
thoughtfully organizing helps to calm the mind.”. Meaning that the physical
cluttering generate our discomfort, since our inner cognitive objective is to
reduce entropy, or organize everything in our mind. The best model will allow
us to move from place to place in it easily, perform new actions, and produce
conclusions/solutions with less effort. In summary, everything can be
considered as a problem-solving task. For example in communication: in
converting will into action and vice versa, in answering a question or in
explaining, and in organizing knowledge to make sense of it. More about in
Section 3.4.

This main cognitive will results with an understanding, or the ability to
control any aspect in the complex model. So in a sense, we have two wills
governing our cognition: controlling and subsequently making sense. In other
words, to accomplish what you want you need to learn how to manipulate your
environment. Obviously, these wills are enforcing each other. It also relates
to the learning process. Either it should be performed via learning some model
(e.g. by observation), and then learning its control model (by
interaction/intervention), or they can be learned simultaneously, i.e. learn a
model efficiently so that it would be utilized in the future.

###  3.4 Problem-solving and Designing

####  3.4.1 Problem-solving

Problem-solving via System 1&2 perspective

A particular topic that involves will is problem-solving [Blocks, 2010]. It is
the expression of human will in a constrained reality (otherwise any will
could be realized immediately). It is a broad topic, which is about handling
any given situation, and not only solving puzzles/mysteries/science. Unlike
classical AI methods to solve problems, e.g. by searching for similar ones in
memory (recording cases), or by finding similar ones and adapt to them in
various ways (case based reasoning), here it has much more flexibility. In a
given problem, a response could be either to recognize a previous similar
pattern (System 1555Based on [Daniel, 2017]), and apply automatic reaction,
i.e. immediate resolution, or try to generate a new solution (System 2).

System 2, in our opinion, is representing the cases when wrong top-down
prediction requires higher level adjustments, see Hawkins and Blakeslee
[2007], i.e., System 2 is the learning mode, tackling new or complex
situations, requiring special attention. After enough training and repeating
of the same problem in system 2, it becomes automatic and decline down to
system 1 for fast response.

Moreover, System 2 is generative. It means that more generally, problem-
solving is generative, while only if possible is discriminative (in System 1).
This generation can be expressed for example in explaining. One evidence for
generative nature can be seen in DL, where explainability is expressed
sometimes via special DNN, e.g. in image captioning [Vinyals et al., 2015],
where the caption is the explanation of the image. However, explanation may
include also summarization or finding the essence of a given group of
elements. Hence, another evidence for the generative nature is in
dimensionality reduction techniques, as Principal component analysis (PCA)
[Abdi and Williams, 2010], where high-dimensional input is mapped into a lower
one which in turn predicts the correct class, thus the low-dimensional space
acts as the explanation space. Similarly, sparse or denoising auto-encoders
[Bank et al., 2020] reproduce the input after corrupting it and compressing
it. These two examples from DL, strengthen our AKREM (or MOM) hypothesis about
grouping. It is since we regard essence of a group to be a lower dimension of
a higher one that includes all the details in the group. Note that,
explanation or summarization in such group hierarchy could be done in any
level of the hierarchy. That is, most concise is in the higher level, while
the most detailed is in its lowest level.

System 1 can be viewed as an effortless bottom-up triggering system, coming
externally to us, while system 2 requires our effort, i.e. it involves our own
will coming from above, so it is a top-down system in this sense. Note that
system 1 is not stimulus$\rightarrow$action, or simple model recognition and a
response, but rather there is always a will present and a hierarchy,
representing the context of the situation, only that we decide whether to take
the fast response (system 1) or the slow one (system 2).

In summary, as illustrated in Fig. 4, you can either decide upon a set of
actions, without any cognitive processing, a kind of straight-way response
(System 1), or you can have possible cognitive operations, before deciding
upon the most proper response (System 2). System 2 can be regarded as problem
solving, i.e. via first divergent thinking, finding possible
solutions/approaches, then applying convergent thinking, to decide upon most
appropriate solution.666Similarly to competing AGI modules over WM attention,
in Global Workspace Theory (GWT).

Figure 4: Short to Long paths between inputs and action

A side note, is that we can view System 1 as automatic first response, that
occurs always, while System 2 can be activated afterwards if necessary.
Methods like Relaxed-Planning-Graph (RPG) from classical AI can be used to
find an unconstrained fast solution of shortest path between current and goal
state, to act as illuminating the different paths from the initial state to a
goal one.

Divergent thinking could be counter-factual one, i.e. “what if” planning, like
in chess, where you contemplate about what will happen if you decide to do
some action.777In this case we are aware of it. In other cases it could be
sub-conscious. Which is why in our modeling the knowledge is dynamic, and can
always produce new outcomes by the set of admissible actions each model has.
This thinking could obviously form a tree-like branching, since we could
perform few steps of ”what if”, in series. Also it can be hierarchical, as any
memory, evole not also in breadth (tree-like), but also in height.

As can be seen in Fig. 4, intuition is the shortest path for response.

Problem-solving in MOM

In this section, the System 2 response is discussed, since System 1 response
is a simple pattern-matching. This process is modeled via 2-phase process in
state space:

  * •

Refinement: starting from a general will to get out of a problematic state,
then deciding upon some goal states to be reached. It makes the will more
definite (directional/purposeful), see Fig 5.

  * •

Realization: searching for a solution via coarse-to-fine hierarchy of models
(Fig 3(a)).

(a) 1: the problem

(b) 2: will turns into a purpose

Figure 5: Gradual will refinement in problem-solving

In refinement, a vague will turns to be more precise and directional. Since
will derives action(s), it is represented similarly to an action in the state
space - as a vector, transmitting one situation to another. Meaning, will
defines the direction the agent wants to move, before it found the
admissible/legitimate/allowable way to realize it, in the given environment.
Finally, the agent starts to plan how to solve the given problem, under given
constraints, i.e. where one cannot fulfill its will directly, but instead is
looking for some legitimate way to accomplish this, in the given
circumstances.

In summary, Fig 5 describing the gradual change, where at first the purpose is
vague, hence a large space can represent the goal state. Then, as the will is
refined, it means more constraints are added or more descriptions of the goal.
In other words, the goal is shrink from something general and vague, to
something more specific. In perception it is the opposite. We perform inverse
engineering. The basic understanding is to know what happened/was done. Better
understanding is to know what was the purpose (behind the actions). But the
full understanding is also from what problem this purpose was yielded/created
from.

Note, that will, after refinement, is merely a sketch of initial and goal
states. To realize it, the will is used to scan for models of different
levels. It is like an attentive beacon(s), focusing on potential models, see
more in 8.2.

The Fig 3(a) figure demonstrates a coarse-to-fine hierarchy, where the will
along with its refinement, is placed at a top level. This level is vague,
since nothing is perceived clearly about the ground level. However, descending
the levels reveal more and more details, and get the will more closely to
realization. It is similar to the process of zooming in on a geographical map.
Higher levels propose general models as a potential stations in a possible
trajectory from a problem state to a goal state. Then at descending, finer
models (with more details) are proposed, consistent with the upper levels, to
move from a problem to a goal state.

This idea is similar to the idea used commonly in problem-solving [Zelikman et
al., 2023]: breaking a problem to smaller sub-problems, and then solving each
separately, and finally combining them into one whole solution.

The search at any level can be performed by any heuristic/learned model, such
as back/forward chaining, Depth-First Search/Breadth-First Search techniques
[Al-Ajlan, 2015], or any combination of those. However, more generally, it is
dictated by will.

Note the mismatch between the lowest model level (proposed solution) and data
level (detailed solution), in Fig 3(a). It is due to our inclination towards
abstracting, i.e. memorizing the essences and less the details
[refformemorychangingwillretreivingit], which is essential for efficient
learning.888Philosophically, it extends into separating models from reality
totally. For example, the assumption of real figures behind the shadows in
Plato’s cave, or the idealism where presumed ideal objects out of this world,
where only coarse instances of those are experienced in our world. Or the
space-time curvatures of Einstein’s general relativity, a model that cannot be
detected directly. It is described also in 7.1, where it is better to learn
several patterns than to loose yourself in a non-pattern realm, where all we
see are details.

Also note that there are other configurations of problem-solving, such as
inference: given specific start state and specific end state, to infer what
happened in between. For example, at 12:00pm child left alone in a room with a
closed cookie jar containing a cookie, and at 12:05pm it is observed that the
jar is closed, but the cookie is nowhere to be seen. This inference also
happens in every information perception (as in a story), where we required to
fill in missing details. Also, in retrieval from memory, many details fade
with time, e.g. time indexes, hence inference is used to extract the time
something happened (e.g. if it happened in elementary school, with Sarah as
teacher, the I must have been in 4th grade). But most likely, inference occur
not only in interpolating (inner missing information), but also in
extrapolating, i.e. forecasting how the plot develops, or proposing several
plausible developments, while reducing the wrong ones as the plot is revealed.
Just like investigators.

All these examples are also supported in neuroscience, e.g. [Hahamy et al.,
2023], where just like as in AKREM, MOM is also inferring by traveling the
different hierarchies/memories, within them and between different ones.

Other configuration could be also in cases where the problem requires a long-
term process, such as succeeding in educational course. These long-term goals,
can be partly active in the background, either to be solved (or partially
solved) during a mundane tasks, or through acquisition of new data and
extracting the relevant information from it to the background problem.

In summary, this approach is non-local, i.e. similar to Means-Ends Analysis
[Sweller and Levine, 1982], it is looking simultaneously at the whole region,
only within different resolutions. It is also cyclic and non-linear, both in
the will-refining stage and in the realization stage. At will refining, it is
since sometimes the goal states cannot be reached, so other states are needed
to be generated, sometimes as a compromise. At the realization stage, it is
since descending in levels might result in conflicts or failures, due to
misalignment between the lowest models of reality and the actual reality.
Hence, returning to higher levels for trying different solutions is needed.

####  3.4.2 Designing

While in problem-solving, the will was generated from a problem, i.e. growing
from an initial state, in designing it is the opposite. Here, instead, it is
growing from the final state(s), searching for the best state to start the
full solution from. It is like creating a story backward: starting from the
end, to reach some beginning (Fig 6). In this case the final states are given,
while unlike problem-solving the initial states are extended into a region (of
possibilities).

In designing there is a goal and a will to go there, but no specification of
the problem or the initial states. Hence, it is an iterative process, see
general algorithm 1. Each iteration starts from searching for a problem to
reach the goal, then continues with a specific will connecting the problem
with the goal, resulting with a problem to solve, thus formalizing it as a
usual problem-solving task (Fig 6).

Algorithm 1 Designing Algorithm

while not satisfied do

search a problem to reach the goal

try to resolve problem-solving task

end while

(a) 1: Design task

(b) 2: Problem-solving task

Figure 6: Designing approach turns into problem-solving task

####  3.4.3 Summary of Problem-Solving and Designing

One can spot a duality here also. On the one hand, problem-solving is an
analytical perspective, looking for a resolution or finishing a problem, by
usually a systemic view, breaking it to parts and then looking for some
appropriate solution, that serves as a better state than the one we started
with (problem state). On the other hand, designing is based on a holistic
perspective, where instead of finding a fast/analytical resolution to a
problem (“to make everyone happy and go on with our lives”) it is about
empathy/consideration, i.e. it is about looking for the roots of the problem,
and not just shutting it down quickly. It takes the opposite approach: instead
of reducing the problem, it tries to track its sources and thus solving the
causes that generated this problem state/situation in the first place. By
doing so, it searches for a better problem to solve, a root problem, which
solves multiple other problems too.

Nevertheless, both problem-solving and design are non-linear in the sense that
at each stage, we can skip backward to previous stages or cycle through the
same stage several times. Also they are both have evaluation mode where the
agent stops what it does to evaluate the whole process efficiency. For
example, by checking if the current state can be considered as the final
state, i.e. whether it holds with all the requirements for the solution. Or by
checking how far the current states of the different trajectories to the
solution. This non-linear cycling is like moving up and down in the hierarchy
of wills.

Additionally, we can see designing as specific and private case of problem-
solving, only now the “given” is different.

In summary, will is either starts from getting somewhere (goal) or getting out
of something (problem), similarly to our basic wills to feel good and to get
away from what is considered bad. In terms of explaining, ”problem” is
explaining the will’s outward directions, while ”purpose” is explaining the
will’s inward directions (or its realization process).

Side note: searching for a trajectory to solve a problem can be also performed
by inversion. For example, proving by negation, or explaining something not by
usual causality but via contrastive manner, i.e. how other things are invalid
in order for deriving the desirable outcome [Gilo and Markovitch, 2022].

###  3.5 Will Field

As been mentioned previously, if actions are vectors, then will is also a
vector. This fact may imply that the direction property of the action is will,
i.e. will is aligned with actions, to solve problems efficiently and fast.
Thus it creates a field in the state space, which helps us to solve the
problems we encounter. Hence, we have separate attributes of an action:
consolidation measure and directionality of an action. It is like
electrical/magnetic field, where the problem state can be considered as ”+”
charge for example, see Fig 5(a), and a goal state can be considered as ”-”
charge, see Fig 6(a). Consequently, when both appear, it defines problem-
solving task, as seen in Fig 5(b). Of course, more complex structures are
allowed, i.e. with multiple ”particles”.

We can see demonstration of will field in searching for solutions in a given
problem-solving task, as described in 3.4. See Fig 7(a) for System 1 solution.
It is the first most intuitive solution that pops up, and it is almost the
shortest path from problem to goal. However it is not considering constraints
of the problem and of logic.

Hence, for the real (or a better) solution we try different paths, either
close to the intuitive solution or far from it, see Fig 7(b) for System 2
possible solutions.

(a) System 1 solution

(b) System 2 solutions

Figure 7: Planning – From Will to Knowledge models.

Note, that in this figure we see the alignment of will and knowledge models,
in the same state space (see the will field in the background). This alignment
support organization of knowledge (models), which is necessary for effective
problem solving, instead of blind search. So the AI works hard in
organizing/aligning the actions, in order to work with minimum effort in the
future, for fast response.

The directionality can be in high dimension, since will can be a complex
multi-facet vector with different aspects. Similarly, it could be like Capsule
Nets [Sabour et al., 2017]. Still you should note, that we represent will as
an arrow/vector, thus assuming its basic form: either moving away from
something or moving towards something.

This idea can be attached to different human feelings, such as disgust,
horror, nostalgia, relief, surprise, and more. See more in Fig 10.

Figure 8: Human’s emotions and their relation to will

where in green: ”+” will, i.e. will to go to (goal); in red: ”-” will, i.e.
will to go from (problem); in blue: unrelated emotions to AGI, like meaning of
life, etc. Also the sense of self model, within AGI and other agents.

This idea may explain why most of our problems are already aligned with will
from previous experiences, mainly being instructed or taught by other people.
Only very limited amount of problems are actually being solved from scratch,
without alignment with will, which makes it feel like searching in the dark.
It is an exhaustive search, where we are the first pioneers to create this
alignment for others. As if we chart/pave a new path in a geographic map for
others to use.

This idea may be contemplated philosophically or religiously, claiming that
the real human is a non-physical spirit/soul, attached to a physical body.
This spirit has will. Thus in order to manifest this will, the spirit must
somehow align it to the models it learns from the physical reality.

Another issue, is what constitutes a state in state-space. Probably it is a
collection of classes (and their states), but then how a collection of objects
acquire a direction, and how is it implemented to its items? One option to
solve this question: actions are already operate on several objects, so we
could say that they have direction, to change the state, even if partially,
towards the goal. Hence, it is ok to define single action’s direction.
Alternatively, we can set a direction to sequential action, i.e. action that
is a recipe-like (composed of sequence of actions).

Regarding problem-solving, after a will is settled, the first reaction is of
System 1 type. This intuitive solution is usually very close to the real
solution. It is often only needs a small tweaks. But if it is not enough,
System 2 starts to search for partial paths (Divergent thinking) which finally
combined into one solution (Convergent thinking).

This alignment is what makes organization of knowledge (models), which is
necessary for effective problem solving, instead of blind search. Hence we
work hard in organizing/aligning the actions, off-line, in order to work with
minimum effort in real-time (on-line), for fast response.

More basically, the idea of will being aligned with actions is evident in new
born infants. Their basic learning is about relating the will to body control,
e.g. when one wants to raise its arm.

Finally, as seen in Section 3.4, there are two separate spaces: models state
space and will space. However, just as complex processes occur in models state
space, there could be complex processes in will space. For example, the
current will may derive from primitive emotions or from higher values, such as
justice, respect, empathy to others (consideration, cooperation), success,
equality, and more.

Nevertheless, our hypothesis that the knowledge map itself is dynamic and
dependent on will imply also that anything can be justified. That is, the
basic problem-solving platform most often can find a trajectory to accomplish
this will. Hence, the will dictates our perception and conclusions, while the
rationality is merely a tool to realize and justify will.

###  3.6 Basic Wills

Although there are wills to solve problems, as discussed in previous sub-
sections (will field and problem solving), there are fundamental wills that
guide throughout our whole live in acquiring knowledge. It starts from the
moment we born and continuous all the time. Our curiosity and wonder drive us
and thus generate basic questions, e.g. Wh-questions (where, who, by whom,
what, how, when, what, why, etc) or any other type of questions. These
questions represent our basic wills to understand what we perceive. For
example, in reading comprehension of young students. This idea resembles to
the RRG method for language parsing Van Valin [1995].

It means, that we induce these basic wills during perceiving a story/message,
even when no goal states exist. However, they are also guide us when a problem
is conceived. In such case they can act as micro-wills, serving the macro will
in the scope of the whole problem. These basic wills serve as guiding wills in
inference, especially for completing missing information and more generally
for the most important reason of all: understanding. It is like anything we
perceive is like a mystery or a riddle, and our mind always tries to make
sense of everything in the perceived message. Part of this process is also in
cases where inference is confident and tries to predict what comes next, in
order to evaluate how the current models are really confident (which if not -
require some resolution or model update). With such interpretation we can
picture these basic wills as small scanning/scouring devices/bots or probes,
that are being sent first to examine the surface (see best analogy in
”Minority Report” 2002 movie with Tom Cruise, sending spider robots to scan
people’s eye for identification). In our case, scanning the surface is for all
relevant facts and knowledge that can be gathered to solve the given mystery.
Then these probes fill in missing information and inconsistencies.

Perhaps these basic wills are the most primeval wills, rooted deep into what
defines us human. Hence, these are usually aligned in early development, and
frequently aligned also throughout our lives. Moreover, since they can be
thought of as preceding the knowledge acquisition, they can be predefined in
MOM, as prior knowledge of the system.

See how inference is executed while perceiving data in Fig 9.

Figure 9: Inference while perceiving

Given a knowledge map (gray network in the figure), we assume that at some
instance we perceive an input, which triggers some object in this map (the
circles in red). Then an inference process starts to characterize this object,
by propagating and activating relevant features999We are a bit ahead of
ourselves: this is Forms or OOP representation of knowledge, with objects such
as concepts, and their attributes/features that characterize them. More about
this formulation is in 5.3., either via 1 step or more. But the inference
scanning is not blind. As mentioned above, it is usually also guided by will,
the basic will, in a form of a simple question. This inference occurs not only
in understanding, but also in planning, specifically to comprehend the problem
and the goal.

As we will see later, these basic question-like wills are guiding us not only
in the micro of inference, but also in stitching larger components in a given
story, to make sense also in the macro. Meaning, not only its function is in
filling missing common data, but also in filling missing parts of a story,
finally resulting with a fully-explained story.

###  3.7 Types of Will

Additionally, there are different categories of will, such as chronology,
causation, and purposefulness. In stories, they are very intertwined/mixed. It
is because purposefulness is a higher manifestation of will (usually applied
in humans), while causation is a lower one, usually applied to animals/objects
(e.g. ”A causes B”), and chronology is simply the way will is implemented: in
a delay. You first want, and then you try to accomplish it. Or in the case of
causation, there is a law, as a fixed kind of will (e.g. gravity), and then it
is realized. See more in Fig 10.

Figure 10: Types of will

These types of will are different qualitatively: objects do not will at all.
It is an enforced will, that lacks any freedom to resist it. Animals have
instincts which at least externally present some kind of choice, or at least
some degrees of freedom, even if it is highly predictable and physically-
constrained. Humans on the other hand, have much higher freedom (free-will),
to be aware of their will in the first place, and then to govern it.

In other words, we could say that _Desire_ is ”a strong wish”, which is
usually not controlled by you, but instead controls you, hence it is more
closer to physical level, since it is more predictable, and less vague/random.
While will is assumed to be free, i.e. uncontrolled-by/independent-from the
state of environment. Therefore, will can be an internal variable in a model
(similar to Einstein’s famous rejection to Quantum Mechanics, assuming that
there are hidden variables). It is a hidden independent variable, due to the
assumption that it is not affected or dependent on other variables, such as
input variables perceived by the senses. But as discussed above, its
independence has measure (from being highly independent in free-will, to being
highly dependent with physical phenomena).

Finally, will is everywhere, in all of our daily interactions. We try to
figure out animals’ intentions or people’s hidden will, to make sense, or in
our case to make our model complete, i.e. enable prediction.101010Model
completeness drive us to search also meaning of life and existence, found
usually in religion or philosophy. Since a specific human has his own will, he
tries to figure out other people’s will to enforce its own will over theirs.
Sometimes it is via competition (if wills are in conflict), while sometimes it
is through cooperation (common will, when it is more beneficial to work
together then solely or in competition). Hence the rise of lying/deceiving
phenomena (either learned from observation or inherited), which introduces new
models to learn. It is beyond good or bad. It is simply modeling for the
purpose of control, or the manifestation of my will, whatever it is. Moreover,
will might be a complex phenomena. That is, it is dynamic like the wind (as
spirit in spirituality). For example, it often can be a mix of wills, even
wills in conflict [Heckhausen and Heckhausen, 2008]. Nevertheless, in this
paper will is considered as a single entity at each moment, to simplify the
discussion111111Although it can be modeled simply by superposition, i.e. by
summing different wills as any mathematical fields..

However, how is will actually included in MOM? It is discussed in 5.3 and
Section 8.2.

Another possible categorization of will is represented in Fig. 12, see the
summary sub-section below. But additionally, we can have other categorization
of will. In this case, we assume that will or some kind of objective is always
presented in human/agent, in all cases. Hence, we are not passive while
hearing/learning/perceiving. Similarly, there is no encoding/decoding will, as
illustrated in Fig 3(b), since our main will is always exists, and the
deciphered will is still under this main will. See this kind of categorization
in Fig 11.

Figure 11: Cognitive operations under a category of the main will.

Example for outward will is explaining. It is like going over the trajectory
of an hierarchy (see AKREM). While in acting, it is like performing physically
or executing a given hierarchy.

###  3.8 Operational modeling summary

In conclusion of this chapter, we can define cognition a shown in Fig. 12.

(a) Cognition parts

(b) planning verse understanding

Figure 12: Cognition definition in MOM.

Following are some additional definitions.

  * •

Will = what affects the actions in state space.

  * •

State = full world information at a given time instance.

  * •

Action = transform a given state to a new state.

All these elements are coexist, aligned, in the same state space. These
definitions are typical definition in many science fields, such in classic AI,
RL, and control theory. Eventually, will, like action, are both vectors in
state space, only we assume that usually will is affecting the preference of
which action to be selected to change the current state. Note that the states
are discrete while actions can be either continuous or discrete also, and the
will is acting like a filter to reduce the amount or the range of possible
actions we should apply to change the current state. This allows a more
efficient and faster problem solving, since the solution space is reduced
significantly. Also note that the state represent only all relevant elements
of knowledge, and not everything that is in the world, since we have a limited
WM. Later, in 5.4, we will define the full model, which defines also the
states and actions themselves, which are composed of basic objects/actions.

##  4 New Associations

Inspired by semantic nets, some general connections are proposed, such as
inheritance (is-a relation), instance property (is-instance-of relation),
part-whole property (part-of relation), an attribute of an object property
(has relation), assigning a value to attribute (value relation)121212Actually
the is-a, instance, and assigning relations can be grouped to one type, since
these operations are all represent specification of some general concept.,
synonyms, antonyms/opposites and more, see [Wiktionary, 2022]. All these
connections are static and can represent stories as separate items, as it is
in AKREM. But to connect these details, as a sequence of applied actions,
operationability is introduced.

##  5 Operational modeling

The two principles added to AKREM are operationability and modeling. Both are
formalized via frames or classes in OOP (Object-Oriented Programming)
[Rentsch, 1982]. Operationability represents the methods that can operate on
classes and change their state, while modeling in our terms is the abstraction
capability131313In philosophy, abstraction is like idealization [Dunham et
al., 2014], i.e. like ”treeness” is the perfect idea of a tree, while all
trees we actually see, are some instances of it. Similarly is the idea of
perfect geometric shapes, like circle or cube..

Both are utilized to construct a general model that can implement different
cognitive functions humans have, and the different models representing
interactions, see ”Models in communication” section in Komarovsky [2022c] and
Fig. 34. To reach this utilization, this model representation should be
transformed into a learnable model such as DNN.

###  5.1 Operationability

It is hypothesized that thinking is operational. Meaning, it is a process,
generating new facts along the way, via some set of actions. Hence, the first
principle added to AKREM is operationability, which turns it from static to
dynamic knowledge representation. That is, unlike static connections as in
AKREM and knowledge/scene-graphs (representing facts or a scene shot) - action
connections in MOM can also be productive (produce new elements). I.e., other
knowledge representations, including AKREM, have pre-defined (or independently
learned) actions and objects, constructing their knowledge base. Similarly is
in rule-engine of an expert system, where there are rules and data they
operate on.

Operationability also allows for maximum flexibility in reasoning, problem
solving, or in finding the proper response in a given situation. It does it by
associating each concept with maximum related operations and attributes. Thus
creating highly connected network of possible actions/paths for huge amount of
scenarios to deal with.

Consequently, operationability adds degrees of freedom to the current
cognitive model, to move in new directions along the hierarchy, i.e. to create
new hierarchies on the fly or update old ones, via admissible actions. It can
be seen in Fig. 13(a), where the previous model, AKREM, had the ability to
move on static memories/hierarchies (instances of associations). But the
current model, is not limited to the options in a given instance, but rather
it can always apply any of its available operations, thus it can create new
associations, see Fig. 13(b). This demonstrates also the effect of past
memories changing over time.

(a) Previous AKREM model

(b) Current MOM model

Figure 13: Current model allow more degrees of freedom

To achieve operationability, a minimal set of primitive operations is
proposed, to function as basic operations, which can be the building blocks
for more complex and composite operations/actions. Operations such as logical
relations (AND, OR, NOT, ”for all” universal quantifier, ”there is”
existential quantifier, (in)equalities, exists, count), flow
operations141414Note that loop operators and if operators can be formalized as
classes, with general methods and properties. See for example at the bottom of
the website: https://www.w3schools.com/python/python_iterators.asp, where an
iterative class with __iter__() and __next__() methods is defined. like loop
operations (while, for) and if-else conditionals, mathematical operations
(+,-,*,/,min,max,norm,log), and other relations.

Note, that loop operations are temporal ones, which should enable both using
the past (current/past variable calculations) and the future (current/future
variable calculations) for planning/simulating goals. For example, future
calculations performed by halting the perception from senses, or ignoring it,
and continuing in an imaginative state of mind. Although it exhibits the
problem of also predicting future sensory input to this artificial reasoning
process.

Note, that ”for all” and ”there is” operations are first-order-logic binary
quantifiers. These operation could become continuous features of classes,
defined in some continuum, such as ”all”, and ”there is” is a range includes:
”most”, ”some”, ”few”, ”one”; and finally ”none”. Thus traverse it into fuzzy
logic. Using De-Morgan laws we can deduct that ”all” and ”there is” complete
each other, while ”none” is the opposite of ”all”.

See in Fig 14.

Figure 14: Quantifiers as features in a continuum

Similarly is in space and time, such as ”always”, ”often”/”usually”,
”sometimes”, ”seldom”/”rarely”, ”never”. And in belief: ”necessarily”,
”possibly” (are complete each other like ”all” and ”there is”). And in not
operation: from ”yes” to total ”no”. Eventually, all these examples, and
different measures (such as of consolidation, certainty, relevance, etc), are
all demonstrate continuous features.

This set of tools described above, can replace DNN units and DNN’s fixed
structure, in a program-search process. It can be implemented for example via
Reservoir network, a random mix of basic rule components/blocks, yielding an
algorithm best describing an operation, within models or between them. Another
way to perform program-search is via Genetic Programming (GP). Also we could
use the AlphaDev framework [Mankowitz et al., 2023]. We could also consider
the idea of Liquid Neural Networks [Hasani et al., 2022] or Neural Ordinary
Differential Equations (NODE) [Chen et al., 2018] as an alternative to the
modeling of temporal operations. These NNs learn spatio-temporal space at the
core, unlike regular DNNs or RNNs. Other possibilities are described in the
Subsection 8.3.1.

At the same time, prior knowledge is needed to be inserted, within operations
and other elements, by including: number of visits, uncertainty, rate of
update, measure of dependence, and measure of consolidation. These variables
are needed for meta-cognition [Ackerman and Thompson, 2017]. The measure of
consolidation is to prioritize different options associated to some element,
to separate the relevant from the irrelevant, e.g. admissible actions, which
can be used inversely in creativity mode - by picking the less expected
directions to follow. Similar idea is presented in neuroscience, e.g.
[Anderson and Barbey, 2022], where the researches state: ”Strong connections
involve highly connected hubs of information-processing that are established
when we learn about the world and become adept at solving familiar problems,
while Weak connections have fewer neural linkages but enable flexibility and
adaptive problem-solving”.

Note the difference between the level of uncertainty of a class (feature or
object) and unknowing. In the former, it is the dilemma when several options
are possible (like multiple versions), and you cannot rule between them. The
latter is similar, only the number of options is different. We can either
regard it as the case of no options, or all possible options are allowed. This
notion is important for formulation of goal states. For example, ”x=?” means x
is an object, it has a feature of equality (=) but the other side of it is
unknown. See math example for this in Fig. 25. Similarly, is in the riddle
problem: ”what am I? I don’t talk, but I reply when spoken to.”. In this case
there is a general class with the assignment feature to some unknown specific
class, e.g. a watermelon or a cat. This idea can extend further to any
inference through exploration in knowledge map. That is, the filling of
missing information, there are small problem-solving tasks, in which the
missing information of some feature is unknown and in the process of become
known. Consolidation, see Section 7.1, can be regarded as the level of
(un)certainty in the class itself.

Moreover, action’s admissibility is needed for several reasons. Firstly, due
to the elimination of entry conditions necessary for an action to be
performed, e.g. on which types (integer, string, etc). This is common in
languages like C#, where all variables’ type must be pre-defined. Secondly, it
is due to the ability to use High-Order Logic (HOL) [Miller and Nadathur,
2012], as in $\lambda$-calculus, which removes any restrictions on an object’s
slot or action’s argument. For example, there could be actions on actions,
e.g. function as an argument in a function or a class decorators in python
which are classes that can modify functions. Hence, relevancy is needed to
constrain action’s admissible space. Additional constraints can be embedded in
relevancy: such as different rewards/costs for applying an action,
time/resources constraints, and more. Note that the problem specifications
determine the constraints, while the will (which can also be extracted from
the problem state) determines action’s features such as relevancy and reward.
And thirdly, it limits the search space in a given problem/request/situation,
by restricting the number of possible sequences of actions. Hence, previous
experience comes to help. Thus, perhaps, this is the trade-off between system
1 and 2, when automatic response is based solely on previous experiences,
while system 2 comes to play when you want to rely less on these experiences,
and to create new ones.

###  5.2 Operationability in Deep Learning

In this section we present an alternative approach, that exists in literature,
to learn objects and actions - via embedding learning.

In DL objects or classes are usually learned via Supervised Learning (SL) and
actions are learned via RL, see Figure 15(a).

However, they should be learned together, where they are inter-wined (unlike
scene graphs with pre-defined actions). Moreover, DNN models should be
learned/updated on the micro and the macro levels, i.e. models of simple
objects/actions or their complex combinations, see Figure 15(b).

(a) Separate learning in DL

(b) Updating both DNN of basic models and their complex structures

Figure 15: Object and Action learning

If they are learned jointly, then the next challenge , is how to represent
operational data, i.e.: objects, actions, attributes, etc. See e.g. in Fig.
16, where the representation is embedded in some feature space, consisting of
objects as points or other shapes, and actions as vectors.

One known theory [Hawkins and Blakeslee, 2007], is that neocortex was evolved
from or on the basis of the old brain, i.e. the
basic/instinct/primitive/reptile brain. Similarly, later in Hawkins et al.
[2019], Jeff Hawkins extends the physical senso-motor system to the abstract
thinking. In our opinion, infant’s first training is over the same principles
in a physical environment. It is what prepares him for next level of
abstracting similar principles beyond physical realm (as in Piaget’s
development theory [Piaget, 2003]).

Figure 16: Operational features space, e.g. in word embeddings

Interestingly, in Bordes et al. [2013] they perform embedding learning over
dataset of triples, each consisted of two objects $(h,t)$ and a relation $l$
between them. $h,t,l$ are high-dimensional vectors, or tensors. This embedding
tries to learn in a high-dimensional space where each triplet will follow the
distance equation of $h+l\approx t$.

Note, that what discussed above is a fixed embedding. This is representation
of knowledge (e.g. words, sentences) in a vector form. Which can make tasks
like text retrieval or usage much more efficient than regular keyword search.
However, contextual embedding is preferred, as in BERT, though it is difficult
to encode such a space, as dynamic one.

Nevertheless, this is encoding, i.e. implicit representation of knowledge, of
sub-symbolic features, not interpretable to us. Hence, MOM proposes a more
explicit representation, of symbolic features. It may be better, since perhaps
all actions cannot be mapped into one feature space, if some actions have no
connection between them (although perhaps all actions could be encoded in one
highly dimensional space, providing a huge amount of features).

MOM represents similar hypothetical space, where individual actions change the
system’s state. It supports different and unrelated types of actions, such as
temporal-only, spatial-only, spatio-temporal, logical, etc.

However, this type of state-space representation is comfortable for
transforming it to other representation, by a simple mapping. This could be
beneficial for example in cases when to solve a particular problem, an
appropriate representation is required.

More generally, this is just one option to represent state space, actions and
will. Other options are also possible.

For example, the vector space representation of state space above, with vector
operations within it is one option. Another representation could be by
attaching some function to every state and thus representing the state space
as some high-dimensional manifold or surface, where problem solving is occurs
not via going from state to state but by optimizing this function and moving
along its gradient to reach some optimum. If we include the will field, than
this function become a vector field function, where each state returns a
vector, and the movement is according to the field and not according to the
gradient. Finally, this function, as a measure of state, is dynamic, and it
changes as a function of will, constraints, etc. Eventually, yielding a
manifold that contains all information of the current problem (will,
constraints) in its shape. This idea resembles the idea of connectionism verse
waveness in neuroscience [Pang et al., 2023], where they claim that in macro
the brain operates more in propagating waves then message-passing among
neurons. Similar idea exists in general relativity, where the (gravitational)
force field is the result of mass distribution in the universe.

Note that though gradient-descent (GD) method, in function representation, is
easier to reach the goal state, compared to classic search algorithms,
however, one enormous problem with it is local optimums and saddle points.
Hence, one way to deal with it, is the ability to see the function not only
locally, but also in higher resolution, if we climb to higher levels.

All proposed representation so far is summarized in Fig. 17.

Figure 17: Different representations of state space, actions and will.

###  5.3 Modeling

If operationability is considered, as the addition of freedom to move in a 2D
knowledge representation, then modeling is an addition of a new dimension,
i.e. converting it to 3D. It is about extending the ”is-a” operation into a
programming abstraction, as in OOP (Object-Oriented Programming), or in
abstract mathematics, such as algebra or category theory151515 Group
properties include identity object to use compositionality of an operation and
its inverse operation. This implies another duality.. Meaning, that while
usually semantic networks represent this operation in a 2D graph, here the
instances are totally separated from classes, and from classes of classes, and
so on. Resulting with a multi-level of abstractions, while for simplicity two
types of levels can be distinguished, in the final LTM, see Fig. 36. In
summary, at first all different associations including operationability are
describing objects, and then abstraction extends objects as instances into
classes, which represent models. Especially, for complex cases like learning-
to-learn, i.e. meta-learning, where abstraction is over similar tasks, or in
transfer learning among tasks. Consequently, unlike AKREM, these full models
are important for natural communication, e.g. for context-based conversations,
where undelivered missing information (common sense) is needed to be filled.
For example, the sentence ”The car was uncomfortable. The seat was too low.”,
indicates that ”the seat” refers to the seat of the car. Or in the sentence
”The kid put his ball in a basket. Then the basket was moved to another room.
Where is the ball?”. Or in the sentence ”Nick is in the kitchen. Jim is in the
garden. Later Jim went to the kitchen. Who is in the kitchen?”161616Note that
in all examples above will can be regarded also as an object, with its
features, such as the objects they are applied on.. These both cases involve
inference to fill up the missing information, which can be regarded as mini
problem-solving tasks, as mentioned in the ending summary of Section 3.3. Note
that common sense is necessary also for intention comprehension, i.e. when we
request something for an AI agent, in the usual minimalistic form, it should
infer all the relevant constraints to accomplish its goal, e.g. for minimizing
SPAM, it will not try to kill all people for that matter.

More over, to establish context, we must define ”settings”, or the context
describing the overall situation. Settings can have also sub-settings and so
on. For example, we can catch a ball moving fast in a game, since we define
the settings we are in. In driving a car, we react fast with controlling the
vehicle and attending the environment, since we first aware of our settings.
This idea also aligns with the temporal hierarchy in [Komarovsky, 2022c],
where in parallel there are the lower layer of high-frequency (fast)
perception-response, then above it and based on it is the mid-frequency (fast)
perception-response layer, and so on. It means that the fast temporal scale
work efficiently if the settings perceived in the layer above are correct, or
fully ”ready” for any data entering. Similar are the settings above the mid
layer, and so on. Settings include also the producing/receiving of language.

The settings are highly important to interpret correctly the situation and
respond appropriately. In other words, the settings or the context dictate
which features are relevant to interpret objects, and it relates or uses only
the relevant actions for these objects.

This extension has several implications.

  * •

First, in a broader perception from senses: from the basic recognition of
instances in AKREM, to a multi-level recognition of instances and classes.

  * •

Next, every element is learned and can be abstracted (into class), i.e.
objects, actions, relations, and attributes.

For example, action with attributes (such as bi- or uni- directionality of an
action), relation with attributes such as strength, numeric attribute/values
as class and its different types as sub-classes (integer, real, complex) in
different bases, group types (strings, sets, tuples, lists, arrays,
dictionaries) with their group operations (slicing, concatenation, union,
intersection, difference, indexing, searching, sorting, replacement, etc.),
and more. Although different types have also their specific attributes (e.g.
set is a collection which is unordered, unchangeable, and unindexed), and
their specialized operations, such as lower/upper case for strings, ordering
operations for non-set types, key-value operations and nesting in
dictionaries, etc.

Moreover, there could be also non-deterministic classes, such as fuzzy sets
and stochastic variables (characterized by some distribution, such as
uniform/Gaussian distribution).

  * •

Next, related to attributes of objects or classes, treated as variables, we
assume that there is no garbage collector in the WM. Hence, legitimate
variables could be only attributes of classes, and no temporal variables are
allowed (unless they are part of temporal classes/methods). Subsequently, our
system’s state is defined as the state of all active classes in WM, which
leaves the methods as purely functional/declarative (not imperative), see more
about Programming Paradigms [Chen, 2019, Van Roy et al., 2009].

Moreover, as stated here SCALFANI , Functional Programming (FP) is much better
for maintenance and updating of existing code. Which in our case means the
ability to learn with as least modifications to previous learned ”code”,
because naturally it requires too much effort.

  * •

Next, this extension enables answering the question about how will is
implemented in MOM, see question at the end of Section 3. Alternatively to
AKREM’s hierarchy by will, which it is not clear how it can be implemented, it
could be generated by abstraction, while the will/intention is serving as an
additional and independent variable in the models that construct the
hierarchy. The assumption here is that humans have will independent of their
situation, while if will is deprived from AGI agent, then this variable is a
function of its sensory input, since it is embedded in the request from the
human user.

Additionally, feeling measures could be included, as influencing will. Our six
basic emotions [Snoek et al., 2023] are: happy, surprise, fear, disgust, anger
and sadness. Alternatively, they can be represented as some high-dimensional
vector, e.g. via valence-arousal model in 2D [Cui et al., 2023]. Although one
paper [Coppini et al., 2023] claims that the six universal basic emotions are
too simplified to describe the wealth of realistic range of emotions, hence
propose special ontology for that.

Moreover, since there are several levels of will, correspondingly there are
the main variable and secondary variables representing these wills, perhaps
with different significance intensities, depending on the abstraction level.

An example for different abstractions could be demonstrated in the word
”bridge”. The simplest form is learned from physical examples of bridges, but
a higher one could extend into function and purpose. Hence it can be detected
also as a log connecting two river shores or ants that form a bridge to fill a
gap, or more abstract notions such as bridging a social gap.

Note, that both modeling (abstracting) and instantiating are localized energy-
efficient memory structures. Meaning, that we do not separate episodic memory
from the base or knowledge memories, nor the need to have separate memory for
abstractions. All of those reside together. This may explain why it is not so
simple to recall events (since they are all embedded in the classes and only
small additional unique cues distinguish specific memories from them). Similar
idea is stated in Crossley et al. [2023]: ”To conserve resources, a brain must
therefore be able to distinguish when it’s worth the cost to form a memory and
when it’s not.”. It may mean that we have the trade-off between being based on
current knowledge and updating it.

Finally, the novelty here, is that unlike DL which performs program-search in
an un-interpretable way, here however, additional inductive bias is
introduced: separating of models (Section 8.1) and performing program-search
to relevant actions (Section 8.3.1), in consistency with other models and
actions. This makes MOM both usable and interpretable.

Side note: Meta-cognition is a similar phenomena to abstracting, since it is
also in levels. The difference, that it is level of thoughts. First-order
thinking is simple experience of sensory input, while second-order is
analyzing/contemplating a memory in which such experience occurred. Eventually
high-order thinking is just pilling up memories of memories (or hierarchies),
watching one over each other. Hence, it is like extension of the hierarchy
representing a memory, into a hierarchy of hierarchies. This occurs also in
stories and any messages, e.g. ”He said that he was thinking about sailing”.
Note that meta-cognition, as probably any hierarchical process (as
abstracting), evolve gradually from childhood.

Alternatively, high-order thinking can be regarded as any hierarchy, i.e.
connected to different parts, and not separated. For example, the sentence
”Bill claims his cat chases mice”, is attaching Bill to a group object, which
consists of the details in ”his cat chases mice”. See this example illustrated
in Fig. 24.

More generally, for simplicity, there are only two cognitive operations with
memories: construction and retrieving. This way the examples above can be
explained as following:

  * •

1st order thinking (observing) $\rightarrow$ construction, from basic LTM
concepts.

  * •

2nd order thinking (recalling a memory) $\rightarrow$ retrieving or traveling
the different hierarchies/memories.

  * •

3rd order thinking (meta-cognition) $\rightarrow$ construction, attached to
past memories.

Finally, just as any abstract thinking, e.g. ”what if” counterfactual
thinking, or high-order thinking (meta-cognition), knowing what others knows,
etc - are all based on the same mechanism of constructing in height
(hierarchy). This operations used in many cases, e.g. in comparing old
knowledge to new incoming knowledge, and update it accordingly, or in any
problem-solving scenario, such as imposing time constraints for solving a
problem in high-order hierarchies.

###  5.4 Summary of operational modeling

In MOM, we represent each object or class along with its features and actions,
or in the equivalent graph view on the right, see Fig. 18(a). Also we can see
how each element of an object has its own measure of relevance, or more
generally it could be other measures, all treated as features in the spoken
class.

(a) Frames or classes in OOP

(b) State and action definitions

Figure 18: State, object and action definitions in MOM.

Also, as in 3.8, we present in the following some additional definitions.

  * •

State = group of relevant object classes and their feature classes.

  * •

Action = learnable algorithmic class.

  * •

Will: derived implicitly from state, and it dictates relevant actions.

After the definitions of knowledge basic elements as described in 3.8, we can
represent a state as group of objects, and actions as those that operate on
one or more objects in a state. The actions are learned, and the will is
specified usually in the first states. How will is derived from the state is
elaborated upon in 5.4.1. As we showed previously, the will and the knowledge
models are aligned, so that the will would be guiding the state to select the
most relevant actions.

Additionally, actions operate in a continuous spatio-temporal space. Meaning,
just as it can act in any direction and region in space, similarly it is in
time: it can be either immediate or include past/future of any scale. This
also enables causality modeling.

Finally, the inference process, described in 3.6, occurs not only in
understanding, but also in planning, specifically to comprehend the problem
and the goal. Therefore, we can view understanding/planning as the macro
processes, with external will supplied to the agent from the outside, either
in the form of some problem to solve or in the form of hidden will that needed
to be extracted. On the other hand, inference is a micro process, derived from
internal will (to make sense), and it occurs during the construction of the
macro process described above. The micro process evolves within states, while
the macro process evolves between states. See summary in Fig 19.

Figure 19: Every change (small or big) is accompanied with a will.

In conclusion, every change, either in features or due to actions, is always
guided by will, or as frequently as possible.

####  5.4.1 Will derivation from state

As mentioned above, will is derived implicitly from state, i.e. from
perception. From the most direct approach, as in the examples in 6.2.2, with
will derived from the first constructed state(s), to the most indirect one,
where it is very difficult to reconstruct it, and it is usually derived from
long process of aggregation.

Hence, we can derive the following equation:

$\text{state}\Longrightarrow\text{will}\Longrightarrow\text{action}$ (2)

Our puzzle here is to figure out how the will is derived from state(s). As
stated previously, will is learned (given only the states), in the evolution
phase of MOM. However, this claim is too general, while at the same time in
many examples the will is specified explicitly in the state, i.e. the learning
is very straightforward. See examples of the math problem, the robot request
or the simple problems in 6.2.2. This is true mostly for inanimate objects,
see Fig 10.

Figure 20: Will derivation from state.

But there are many cases, that the will is implicit and hidden from us, such
as for animals and humans. See ”state$\rightarrow$will” row in Fig 20171717Fig
20 is an extension of Fig 10., where animals are driven by hidden inner
emotions. Though humans can also act upon emotions181818While Descartes’s
philosophy of humans states that there are two modes of thinking:
understanding and will, Hume’s theory on the other hand makes the rationality
the slave of emotions, instead of the opposite common view where it is who’s
in control. This is our assumption also: the will dictate everything. It then
use cognition as merely a tool to fulfill itself or to justify itself, no
matter how selfish or horrible it could be., however some people acquire a
second nature, that override emotional responses. It is usually derived from
some set of values or beliefs. This set is represented by rules, and it is
similar to language rules (see Fig. 22), acting as an interface between two
entities.

However, any set of rules is also deterministic, and so is the derivation of
will from emotions in animals. See ”to it/him-self” row in Fig 20. So how
animals and humans models are still stochastic?

Indeed these three mechanisms are deterministic by definition. However it is
not the case for an outside observer or listener, see ”to an outsider” row in
Fig 20. For the animal it is usually hidden, while for the human there is also
a second factor, the free will, struggling between these mechanisms, to choose
to behave accordingly to values or not.

Finally, all these mechanisms as perceived by an outsider can be modeled in
three forms of states and their admissible actions, ranging from totally
deterministic as an ”if-then” rule system, up to totally stochastic, see
”inside state” row in Fig 20. This represents admissible actions as either
multiple or single.

For some examples of these three forms, see sub-section 6.2.4.

Note also, that studies show that including emotions in some event assist in
memorization of it. It can be seen when monotonic type of talking reduce the
motivation to remember or even to listen (may even cause drowsiness). This
effect might just support the importance of will in any message.

##  6 Practical MOM in Mature state

In this section we start with some thorough examination of the proposed
knowledge structure in MOM’s mature intelligence state, especially grouping
(either in space or in time or in both), following with different examples.

###  6.1 Knowledge structure

Note that causality is a special case of modeling, a spatio-temporal one,
where re-occurrence is consolidated. More generally, re-occurrence helps in
learning both static objects and dynamic basic/composite events (equivalent to
scenarios/scripts in OOP). Usually learned events are only the basic ones,
i.e. in micro scale, because compositions of these are too many, and it
becomes a combinatiorical problem to remember all possible compositions. Only
few, very common composite events remain. Note that events can also represent
common scripts, recipes and algorithms (can be used in problem-solving for
example). Which is why in our opinion, basic algorithms, may use FP, while
time-sequences, event-related algorithms, that similar to planning, are of OOP
type.

See more in Fig 21.

Figure 21: Aggregation of basic elements to composite ones.

Moreover, additional prior knowledge can be inserted in recording events:
date/place markers for later recollection191919It could be done for example by
attaching the relevant spatial and temporal attributes to the particular event
class. Especially it is needed for temporal relativity between events, i.e.
for after/before relation.

Some clarification so far: objects regarded as static, while their actions can
be involving time, e.g. a dog object has a ”running” action which when applied
transforms its current state to a new state in the future202020This running
action is attached to the dog object as a feature, as long as the action is
operating.. Scripts are classes that contain a sequence of causally coherent
event classes, i.e. each event causes the next one 212121Philosophically,
David Hume’s theory of causation states that cause and effect relationships
are not a product of natural law or universal truth, but are instead based on
the necessity that we associate events based on experience, i.e. it is yielded
empirically, or: we infer it from past experiences. Hume argued that causation
does not exist in the physical world, but rather is simply a construct created
in our minds. On the other hand, causality by Immanuel Kant is a priori
embedded in our mind, i.e. cause-and-effect is a prior assumption, even before
data entered our senses. In our humble opinion, it does not matter that much,
since causality indeed inferred (either a-priori or not) mostly unconsciously,
e.g. at sleep. What matters is that causality is part of the will system,
where it is a tool to gain control, or realize the will.. But in order to be
fully informative, it includes also all the background of these events: entry
and exit conditions, objects involved in the events, and tracks - variations
of the script, i.e. different options of event sequences. In other words, it
is a flow program of ”if-else”s made out of different events. An event can be
either basic, i.e. made out of primitive actions, e.g. via simple sentence
”The dog ran home”, or it could be composite (a scene), made out of basic
events. A script can be part of some object, e.g. Eat-at-Restaurant Script is
Event-Sequence slot in a Restaurant class.

Additionally, since scripts have many possible tracks to follow, it can be
modeled usually via method, representing nested if-then structure (tree-like),
to represent all possible common tracks. Actually, even the default sequence
of events in a script is already a method, and it is belong/applies to the
script object. Hence, knowledge should not be represented merely by nodes and
edges as a simple graph, but as a hyper-graph [Bretto, 2013], or as nested
graph [Angles, 2009], where entities can also be groups. So we can abstract
our original meaning of grouping (part-whole) not only on static info but also
on a dynamic one, i.e. group of events. This grouping introduce yet another
type of hierarchy in our system, besides the abstraction. Or we consider
grouping as matching some general class to the given details. Hence, there are
two independent types of hierarchies that can evolve in the system. Both could
produce temporal classes/instances, which do not need special memory, but the
usual one, where their usefulness decay rapidly if not reused.

Moreover, gaining essence out of grouping (as it is in AKREM) is a difficult
challenge, but also important for summarization tasks, and for retrieval from
memory. One way it could be done is by generating the meaning of the group,
e.g. by formulating it as a problem-solving task, where we describe the
problem/goal/trajectory that this group represents.

However, in MOM, these formal definitions play out differently. First, there
are no conditions, but rather simple associative triggering of relative
objects (either static or dynamic). Second, it is all based on abstraction
principle. At the lowest level it is an accurate sequence of object and action
instances, which is stored as episodic event. At an upper level it can
abstract different parts of this or/and similar events, e.g. same sequence of
actions but different objects participating them. A higher level of
abstraction can add also abstraction of the specific actions/events that are
involved in the previous episodic events. Even higher abstraction can involve
only the major events that are shared among many episodic events, in a form of
a group perhaps, and not necessarily with specified order.

Note, that this generalizes the recognition in the system, to be not only
spatial (of static objects) but also temporal (of events), hence for example
an action or a set/sequence of actions can trigger some event object/class. An
event class like any class can have its own actions and properties, for
example an action can contain some admissible actions that follow it or
precedes it.

Also, since we can have hierarchical structure not only for classes and
groups, but also for actions, then will field affects this structure also. It
diffuses down the hierarchy and learns also the directionality of inner
actions. That is, there is alignment not only on the actions in high levels,
but also on sub-levels until the lowest level. Hence, we result with some type
of dependence/alignment between the upper and lower levels actions’
directions.

This hierarchy is what illustrated in Fig 3(a), where coarse models are
classes that can represent a group of classes in some structure (e.g. a
group). As stated there, it is easier (less mental load) to plan in such
hierarchy, since you can plan via small amount of large chunks (of sub-
classes), then descend and plan also for small amount of classes, only smaller
chunks, and so on.

###  6.2 Examples of MOM in Mature state

####  6.2.1 Language as separated from knowledge

Before presenting the following examples, we should note that language itself
is a set of models, likewise are the translation between them. These models
are most probably symbolic, i.e. included in the classes of our knowledge
models. Although it might be partially also sub-symbolic (and perhaps be part
of the cognitive models, see 8.1 and Fig. 37). Either way, these models are
utilized in every input/output language operations, such as reading or
listening for input or talking/writing for output. It means that the knowledge
itself is not represented in the form of language, since syntax and grammar
are specialized for each language, while the knowledge itself should be
invariant to the language we use to express it. Language is like our interface
to the outside world. Though thoughts themselves can be accommodated with
language it is still not rooted in the knowledge itself, which is far more
flexible. It, or ”talking thoughts”, is simply our way of simulating
interaction with the outside world, similarly to any planning or imagination
activities.

In summary, Language is separated from knowledge or the actual cognitive
operations, since these operations are more fundamental processes than
language’s specific structure. Language is merely a tool to communicate
thoughts (to myself or others). This interaction is visualized in Fig. 22,
where external process are e.g. senso-motoric activities and internal are e.g.
planning, imagining, simulating, etc.

Figure 22: Language as separated from knowledge.

This side note purpose in relevance to the following examples, was to
emphasize that we ignore the language to/from conversions with knowledge map.

Another note, is that knowledge represented in MOM’s mature state (objects,
features, actions) and wills – are all learned, in the evolution phase. Hence
the knowledge in the following examples is merely hypothetical demonstration,
while in reality it could be different.

Finally, naming, or the inclusion of language to describe
objects/actions/features is not necessary in model learning. The learning
still occurs, even before its name is introduced, or if it is forgotten for
some reason.

####  6.2.2 Simple examples

The simple examples are represented in Fig. 23. Also we can see how each
element of an object has its own measure of relevance, or more generally it
could be other measures, all treated as features in the spoken class.

Figure 23: Simple examples.

In Fig. 23 examples, the cartoon of a head emphasizes that an attention is
split: one part is perceiving incoming information, while the other part is
trying to comprehend the total accumulated information received so far (by
applying inference simultaneously with perceiving). Usually this part of
attention is in the background (sub-conscious).

In Fig. 23(a) example, it first triggers the fish object, then guided by
”what” question it extracts the relevant feature(s). Next it is ready to use
language models to phrase the goal state in a correct grammatical form. This
step is avoided as mentioned previously.

In Fig. 23(b) example, the dog class is triggered, and then an instance of it,
assuming it to be the last discussed dog, is accessed. Again, we use will to
extract the relevant feature. This time ”where” question directs us to a
specific feature: Place, which is a class by itself, with the feature: ”under
the bed”.

Next, we demonstrate high-order thinking, which is discussed in 5.3.

Fig. 24 is an example of this case. To simplify the image we present only the
final state of the given message. In the example we show that high-order
thinking can be expressed as a hierarchy via grouping. Actually, the group can
be expressed in the same level. Also, this hierarchy is similar to part-of-
speech (POS) parsing.

Figure 24: High-order thinking via grouping.

Next, an example of problem-solving task is presented in Fig. 25. This example
is a math problem, described as follows: ”Math problem: given that 2
helicopter toys and 3 car toys cost 600. also given that 3 helicopter toys and
1 car toy cost 550. How much the helicopter and the car toys are cost each?”.

Figure 25: Math problem example.

In Fig. 25 we see several things. First, we see a group object (of Helicopter
and Car objects, colored in brown) in the problem state, and its cost feature.
Next, we see the will induced for this specific problem. Next, we do not see
it in the figure, but there is inheritance: Helicopter and Car objects derived
from Helicopter and Car classes, respectively, and these classes derive from
the Toy class. Finally, we see a short visualization of one proposed solution,
displaying only the main elements in each intermediate state.

####  6.2.3 Complex example

Next example is a complex one, hence it will be described lengthily. It is a
mixed example, because it involves both the part of story comprehension, or
understanding the problem (initial and goal states), and the planning part to
solve the given problem.

Fig. 26 is the result of the following description, revealed as an incoming
stream to the robot: ”Jim speaking to his robot: I’m in a shower. Forgot to
take a towel. Can you bring me one?”.

Next, we develop our discussion about the example in the following sequence:
description of prior background knowledge and settings prior to the event,
then the process of perception and inference (Fig. 26(a)), and finally the
process of planning (Fig. 26(b)).

There is naturally some pre-settings of this request: there is a serving
robot, Jim is a human and he is its supervisor. When Jim talking to the robot,
it needs to perform what Jim asks. Currently they are both in Jim’s apartment.

Some background common sense is assumed to be learned by the robot. Such as
facing uncertainty while receiving a request. Other constraints include:
ethics - do not break stuff (e.g. wall) to get something, nor to ride over
people or animals on the way; actions/states that are forbidden/required (e.g.
embedded in cost feature); and general cognitive memory and time (resource)
constraints: e.g. total number of steps or total time constraint (or order of
it).

Figure 26: Mixed Example: Understanding and Planning.

We can see the simultaneous inference process in the states. For example, in
the initial state, face washing denial and towel’s feature are not given in
the input stream, but inferred. Also, note that the actual representation of
this example is larger. Due to space limits, many details were omitted. For
example, we do not show that “face washing” activity is a wrong branch, even
though it requires also a towel. Since he did not specified this, the robot
can either ask for confirmation/clarification, or assume which of the tasks is
in more demand. Body washing means Jim is nude, and it requires from him to
get dressed to find the towel himself, while face washing is less of a
problem. Hence, body washing is the more challenging activity to retrieve the
towel himself, hence Jim is in need for more assistance in this case.

As can be seen, the states presented in the figure are the act of
understanding the problem, or the story so far, until the robot realizes the
request from the story. This request is what turn this story as incomplete,
which requires some resolution/completion to fill the missing steps of the
story in the state space. This description is typical to most of our
communication, since we regard the incoming messages as some mystery to be
figured out, either by inner inference or outer inference. All for the purpose
of constructing a legitimate full story. Next we describe these two phases.

The process of state generation is as follows (Fig. 26(a)). It first tries to
comprehend the situation: shower, towel. Next it tries to construct the goal
state: by=robot, where=to Jim’s hand, when=as soon as possible (since shower
is a matter of minutes).

We can also see, that state generation is not only performed to the future,
but in some cases it can also be into the past, and not only that but it could
be also not in consecutive steps, but in some skips, such as the skip from the
initial state to the provided goal state. Hence, more generally, a story can
be completed by different time skips, for example forward/backward
constructing, or some random skips to different time lines or space-time
settings.

Next, is the planning process (Fig. 26(b)), or solving the problem, or the
“how”. It should start with the robot being stopping the current activity, or
putting it on hold, or giving it a few moments if it is more important (these
considerations are also important). Next is to start the new activity, of
reaching the new goal. When: should start now. Where to go: to where towels
that Jim wants are (these are constraints in the problem). How to get there:
walk to where they are, take it and go to Jim. This is high-level model. Low-
level is the walking procedure, velocity according to time constraint, while
safety is assumed during walking.

Note that we described the problem-solving as the ”how” question. Relating to
inference guiding basic wills, see 3.6, we can claim that guiding will can
also appear in the macro, for example as the type of will connecting the
initial state to the goal state.

The planning process starts from general actions or wills, and descent to
levels with more details, in order to make it an actual feasible plan. Or to
reach as most detailed solution as possible, see more about it in Fig 3(a).
During descending the actions/wills are decomposed into smaller units for that
purpose. For example, see the ”take a towel” action in the 2nd level from the
bottom decomposed into ”open closet”$\rightarrow$”pick a
towel”$\rightarrow$”close the closet” sequence of actions, in Fig. 26(b).
Additionally, as can be seen, actions and wills are interchangeable. Sometimes
we call it a will and sometimes it is an action.

As mentioned previously, in Fig. 12, planning is a top-down process, while
understanding is the opposite. We can demonstrate it by using the same Fig.
26(b), only instead of looking at it as constructed top to bottom, we imagine
it as constructed bottom-up. It can be done simply by telling what the robot
did retrospectively: ”Jim and its robot were home. Jim were taking a shower.
The robot stopped what it was doing. It walked to a closet. It took a towel
from the closet. It then went to Jim. And then it gave Jim the towel”. In this
case the process involve abstraction/grouping to aggregate the detailed
actions into more general ones. For example, see the ”open
closet”$\rightarrow$”pick a towel”$\rightarrow$”close the closet” sequence of
actions in the lowest level aggregated into ”take a towel” action in the level
above, in Fig. 26(b). Finally we (or any listener) reconstruct the original
will from the detailed actions: ”Bring a towel to Jim.

Also note that at each state generation, weather it is planning or
understanding, there is also local inference processes, which can be regarded
as internal will. As opposed to the external will, which we try to reconstruct
from the story. See more in Fig 19.

Finally, important to note is the surprising comfort of actions to be composed
or decomposed, since the grouping is represented as simple sequence of
actions, while in general the grouping of actions could have any form. This is
not some lucky incident. It is appeared to be basic principle in programming,
see the beginning of section 8.3.1 and its footnote.

####  6.2.4 More special examples

Here we discuss some interesting special cases. First we show how actions,
similar to functions, can operate on several arguments, or in our case on
several objects. See the example of developing equations in Fig. 27.

Figure 27: Developing equations example of actions applied on more than 1
object.

In the next example, shown in Fig. 28, we can see various effects in a story.
The story is: ”David was running, while Jane has been doing the dishes for
over an hour. In his route he met Jim and greet him. In the meantime, Jane
finished to wash the dishes and put a turkey in the oven. … David came back
home from work to Jane.”

First, we see a process (changing object’s state after a while) in contrast to
an instant type of action, in red color: the dish washing, which disappears in
the presented next state (also in red).

Next, we see how objects are added over the states (in green) or removed due
to to the pass of time or forgetfulness (in blue).

And again we can see actions that are applied on several objects (in purple).

Figure 28: Example story with various effects in it.

The next example demonstrates the principles described in 5.4.1. Specifically,
in the following are some examples of the three forms of single/multiple
admissible actions. See Fig. 29

(a) An inanimate object

(b) sub-symbolic to symbolic

(c) Multi-modal perception

(d) Human’s free will

(e) Pavlov’s experiment

Figure 29: Examples of single/multiple admissible actions.

Fig. 29(a) is about an inanimate object. Fig. 29(b) is about sensory
perception transformed into our knowledge map. Fig. 29(c) is about sensory
perception, specifically from different modalities. Fig. 29(d) illustrates
human’s different options to act or react. Fig. 29(e) presents a psychological
experiment, that tried to teach a dog to associate a hearing ring from a bell,
to being food served. Hence it is a reinforcement type pf learning. We see
that before the experiment the dog has no special reaction to a ring
(stochastic reaction), but after enough training we see he is developed a
dominant response in the form of drooling (deterministic reaction), which can
be directly translated to its will of expecting a food to be delivered.

Finally, we see here different types of interactions (sensory inputs and
physical acts) with the environment. And we see that most of the interactions
in the figure can be aggregated into a group object, e.g. event object, with
the appropriate actions/features. For example, ”hit ball” object with the
action/feature of being in the air, ”(audio) horse or (visual) horse” object
with the feature of ”horse” class in knowledge map, ”a ball thrown at me”
object (and its admissible actions), and a ”ring-hearing dog” object with the
drooling action associated to it.

####  6.2.5 Another complex example

In this example we will see how multiple wills can co-exists in a story. In
Fig. 30, for simplicity, actions denoted as arrows, usually perform changes in
several objects to their different attributes (as mentioned in previous
examples). Here high admissibility is expressed via salient color (while
directionality as usually relates to will). Fig. 30 is a MOM representation of
evolving state, representing a story. A state is consisting of several objects
(joining/leaving as the story evolves), including their attributes and actions
that define the state.

The story: ”David entered his room. He searched for something on the floor.
Then he searched in the basket. Then he searched under his bed, and was
thrilled to find the ball there. In the meanwhile, his mother entered home.
She put her keys on the desk. Then she removed her shoes and put her
sunglasses on the desk. Then she searched for David, found him, and they sat
to eat lunch together”.

Note how hierarchy is mostly around abstraction, and slightly about will. For
example, in the story we could switch the focus in the middle of non-completed
will, e.g. ”while searching for something, mom entered the house”. This
example also demonstrates different levels of will. From the first sentence we
understand the lowest form of will - he ”wants” to move in the room. Later a
higher will is introduced: searching for something. Only at the end a higher
will is presented: he wanted his ball. There still could be a higher will
serving these sub-wills, e.g. he wanted a ball to play with his best friend,
whom he enjoys so much. So at first it can be a sequence of actions connected
by time, and as the story evolves, will is revealed, and these actions mapped
in a clear field of wills, within different levels, similar to problem-solving
scenario: problem state and goal state.

The same story is represented via AKREM, without the evolution of details, see
the video link in [Komarovsky, 2022b]. Note, that if repeated often, the
sequence of David or anybody searching in some place can be grouped/abstracted
as an event class, also referred to as Trans-Frame [Minsky, 1988].

Additionally, it is seen that this is a hyper-graph, since the actions,
represented as edges in it, connect multiple sources to multiple targets. That
is, actions, though connected to all objects by relevancy, can actually act
mutually on several objects at once. This is also supported in neuroscience,
e.g. in a modified Hopfield network [Burns and Fukai, 2023], where set-wise
connections instead of pairwise connections between neurons is proposed.
Altogether, the edges and the nodes (as mentioned previously they can be
grouped to represent new group node) imply a representation of hyper-graph.

Figure 30: A part of a story.

Note that AKREM’s examples, and the presented example imply a dynamic
knowledge representation, i.e. watching the full knowledge state as it evolves
in time, while a story is perceived. It is like frames in a movie, stacked one
over the other. See Fig. 31.

Figure 31: Dynamic knowledge representation of the story in Fig. 28.

However, in all illustrations so far, we separated the instantaneous state
from its evolution (state-by-state static representation), to emphasize the
will main effect on how this state evolve.

Nevertheless, it spurs the discussion of which of the above representations is
more appropriate. On the one hand, MOM’s state evolution representation is
supposedly virtual, and should not be grasped/accessed by the agent. On the
other hand, this representation is presented in agent’s mind, while planning,
imagining, simulating, etc.

In memory, we suppose that there are two separated but interconnected
memories: the usual LTM, and some uploaded items from it in the WM. This may
explain the capacity limit in WM. Since if the planning are sequences of
changed state copies, then there is a limit for how much can be held in terms
of states and sequences of them. For example, imagine the size of a memory
needed for a sequence of states in an evolving story, which includes multiple
objects and agents.

Thus, to make the memory more efficient, we can use the dynamic
representation, and at each ”frame” store only the changes from the previous
state. Also it supports our efficient representation of uploaded memories into
WM, as being generated directly from triggered objects in LTM. Meaning, we
maintain layered representation which is founded upon LTM knowledge map.

Likewise, grouping, as discussed in AKREM, might just be for the purpose of
reducing the amount of elements attended at each time instance.

##  7 MOM evolution

This section deals with how MOM is evolve and develop through time, from
”birth” of a human/agent till it reaches its adult phase, or mature
intelligence state. A state we consider as stable.

We do not consider this state as the goal state of cognition, since obviously
the agent/human can still evolve and learn new things. However, we sometimes
refer to it as goal intelligence state, merely to emphasize that this is the
desired outcome of a thinking machine or an agent. A stage of its evolution
that it is fully capable to assist and drive progress in different scientific
faculties. It can do so by solve problems or design new and creative
artifacts.

Finally, this section concentrates mainly on the basic principle of
consolidation in the cognitive evolution, and then it elaborates about the
learning approaches that use this consolidation tool in their process.

###  7.1 Consolidation

So far, the MOM cognitive model has been presented in its mature state. Now,
the discussion is about how to reach it. This is a process in time, which is
mainly based on consolidation. A more general view of cognitive evolution in
AKREM is discussed in Appendix A.7.

Consolidation is about transforming from chaos to some stable order of
patterns, or from a continuous realm to a discrete one, as in quantum
mechanics. An infinite amount of details is hard to handle (i.e. to understand
and then to control), therefore consolidation to fewer patterns is required.
Consolidation also allows for fuzzy logic and categories [Wyler, 1995].

It also allow for effective and minimal communication, with short codes, where
there are few shared symbols.

Consolidation can be expressed in many forms, such as:

  * •

in the conversion of sub-symbolic to symbolic, for any type of element

  * •

in cognitive evolution: from flexible (at infancy) to less flexible (at
adulthood)

  * •

in problem solving: going back and forth between divergent thinking (open up
for multiple solutions) to convergent thinking (consolidating into one or less
potential solutions)

  * •

in modeling, at program search, from huge hypothesis space for possible
programs to a small set of hypotheses (as in DL). It is both in the micro
(within models) and in the macro (between models)

  * •

in testing multiple versions of an unknown model, and finally converging into
less/one version(s) that are/is consistent with evidence

  * •

and in grouping/abstraction, where some separate elements become connected

Note that causality is a special case of modeling, a spatio-temporal one,
where re-occurrence is consolidated. More generally, re-occurrence helps in
learning both static objects and dynamic basic/composite events (equivalent to
scenarios/scripts in OOP). See more about this in Section 6.1.

Additionally, MOM enables multiple parallel versions of the same thing, since
any specific topic or subject can have multiple theories/models, sometimes in
conflict. Hence, like in the quantum superposition realm, multiple-version
combinations could be tryout, and consolidation can help in collapsing them
into fewer versions. Those versions should make the most sense, i.e. to be
consistent on different occasions or supporting the majority of evidence.
Thus, it just maybe, that at infancy, a highly uncertain period, there are
many versions created, and with time - only the most consistent ones survive
(consolidate).222222Note, that it has nothing to do with having multiple
versions representing different perspectives or opinions, over specific
controversial model/topic. In this case all versions are necessary, and not
reduced with time. Similarly when multiple versions of a concept (with a same
name), having multiple interpretations, depending on context, due to the fact
that meaning derived from will. And since there are different wills for the
same objects - different meanings/interpretations emerge.

Interestingly, consolidation is about prioritization of models, i.e., it is
about seniority. In other words, the more ”proven” models are those that ”call
the shots”, e.g. it is required from new untested models to be consistent with
these old models. This prioritization is what allows for symmetry-breaking,
i.e., the ability to go from totally symmetric/equal model proposals and
converge slowly into a more confident and stable models.

Moreover, multi-version principle aids and fit also to the will field. As we
discussed previously, it is dynamic resetting of the knowledge representation
to fit the current will. In other words, the agent learns how according to a
specific will, it should produce the relevant objects and actions to
accomplish this will. It is similar to the idea of problem representation,
where a problem is half-solved if we succeed in representing it in its most
natural and efficient way. This fits perfectly with having multiple versions
of ”world representations”, where the will acts as a switch to select among
those. This is yet another multi-versionality, hidden in the additional
degree-of-freedom: the will. Similarly, settings or context, see 5.3, can
determine the representation of knowledge. Similar idea is advocated in
[Schaffner et al., 2023] where perception is preferred to have goal-oriented
representation over ”seen as it is” modeling. This is also embraced by
attention mechanism, where the sensory information is always overwhelmed,
hence must be filtered and handled efficiently.

Lastly, two operations help in producing consolidation. On the one hand, to
deal with a stochastic environment and ambiguous signals, repetition provides
memory prioritized by relevancy. Hence the need for associative recalls, i.e.
cues. Repetition is never exactly over the same thing, but rather over many
different examples of a thing. Hence, it is useful also for generalization or
abstraction out of diverse and noisy/blur/corrupted/partial examples.
Therefore, the strict/rigid approach (e.g. symbolic or logic) is not suitable
for learning in a real-world or world-simulated environment, but only in
idealized well-constructed one. Repetition is needed also with guided tutoring
of an AGI agent.Conversely, sparsification is about reducing irrelevant
signals. It also support Occam’s Razor principle, i.e. searching for the
simplest algorithm among all possibilities.

In summary, these operations are also act as dualities.

####  7.1.1 Reusability

An additional form of consolidation is reusability, since the more learning
progresses, the fewer new models are proposed in favor of using existing ones.
Hence, reusability is expressed via exploration (mostly at early stages) verse
exploitation (in stable or mature stages), as in RL. In the beginning, many
possible codes are generated for actions/models, but as time goes by, the
process is less exploratory and more exploitative, i.e., there is more
emphasis on retrieving known codes, while testing fewer new codes in parallel.
Similar effect was seen in learning abilities among children verse adults
[Frank et al., 2022]. This effect states that children can gain new memories
easily, while adults exhibits interference with old memories in the process of
gaining new memories, hence an inhibition process is stronger at adults.

In addition, reusability aligns perfectly with abstraction/grouping, in a
constrained environment and limited resources. They are both needed to hold
control of as much as possible, with minimum effort [Zipf, 2016], i.e. without
generating many models of each thing.

In practice, reusability is about using less of the initial available tools,
as the learning evolves. Meaning, while regular DL tools (if, sum, activation
function) or the primitive tools, see Section 5.1, can be used for program
search of basic action methods or relation methods, the new methods apply
reusability. In such methods, less primitive tools are used while the current
methods are used more, thus encouraging more connectivity in the network,
since the more existing methods are used, the more are associations are
connected to them, thus avoiding isolation of new methods by inner program
search. It also encourages logical connectivity in the model, since DL’s non-
interpretable connectivity is replaced with more logical functions, as in
algorithms.

Moreover, Functional Programming can be applied to assist reusability. On the
one hand, the general/outer structure is OOP, i.e. elements are grouped in an
OOP fashion. On the other hand, methods are kept in a pure operational
immutable form [Chen, 2019, Van Roy et al., 2009]. Meaning, having small and
simple methods, which maximally reuse other functions, and without inner
variables, due to objects-memory-only assumption (see Section 5.3). Meaning,
striving for methods that are comprised of other methods, as much as possible.
This is compositionality/grouping applied in actions (similar to hierarchy of
visual features in CNNs). See summary of reusability in Fig. 32.

Figure 32: Actions, objects, compositionality, and reusability

Final remark for this chapter: we encourage curriculum incremental learning
over batch learning or even meta-learning, since our belief is that learning
is gradual, in stages. Meaning, we cannot use shortcut to learning, by
squeezing everything for an agent to learn. It must gradual process, most
probably with failures and regressions. Additionally, we advocate slower
learning scales (days, months), with gradual improvements, in contrast to fast
learning (hours, days) with final model as in DL. Similarly, we are for larger
and more dominant stage of exploration verse smaller stage of exploitation. It
is because we humans use unguided or self-learning, just as supported by many
important AI researchers, similarly we should allow for the AGI agent a long
period of experimentation and exploration, before it converges into a
solid/stiff world/knowledge model. See more motivation in Appendix A.7.2.
Finally, DL is very problematic with regards to data. Because of the phenomena
of garbage-in garbage-out, the data scientist must clean carefully, tediously
and exhaustively the data from outliers, duplicates, ambiguity and other
factors. This both narrows the task, and do not let the model to learn