# Analogical Concept Memory for
Architectures Implementing the Common Model of Cognition

Shiwali Mohan  Matthew Klenk

###### Abstract

Architectures that implement the Common Model of Cognition - Soar, ACT-R, and
Sigma - have a prominent place in research on cognitive modeling as well as on
designing complex intelligent agents. In this paper, we explore how
computational models of analogical processing can be brought into these
architectures to enable concept acquisition from examples obtained
interactively. We propose a new analogical concept memory for Soar that
augments its current system of declarative long-term memories. We frame the
problem of concept learning as embedded within the larger context of
interactive task learning (ITL) and embodied language processing (ELP). We
demonstrate that the analogical learning methods implemented in the proposed
memory can quickly learn a diverse types of novel concepts that are useful not
only in recognition of a concept in the environment but also in action
selection. Our approach has been instantiated in an implemented cognitive
system Aileen and evaluated on a simulated robotic domain.

###### keywords:

cognitive architectures , common model of cognition , intelligent agents ,
concept representation and acquisition , interactive learning , analogical
reasoning and generalization , interactive task learning

††journal: Cognitive Systems Research

##  1 Introduction

The recent proposal for the common model of cognition (CMC; Laird et al. 2017)
identifies the central themes in the past $30$ years of research in three
cognitive architectures - Soar (Laird, 2012), ACT-R (Anderson, 2009), and
Sigma (Rosenbloom et al., 2016). These architectures have been prominent not
only in cognitive modeling but also in designing complex intelligent agents.
CMC architectures aim to implement a set of domain-general computational
processes which operate over domain-specific knowledge to produce effective
task behavior. Early research in CMC architectures studied procedural
knowledge - the knowledge of _how_ to perform tasks, often expressed as _if-
else_ rules. It explored the computational underpinnings of a general purpose
decision making process that can apply hand-engineered procedural knowledge to
perform a wide-range of tasks. Later research studied various ways in which
procedural knowledge can be learned and optimized.

While CMC architectures have been applied widely, Hinrichs and Forbus (2017)
note that reasoning in them focuses exclusively on problem solving, decision
making, and behavior. Further, they argue that a distinctive and arguably
signature feature of human intelligence is being able to build complex
conceptual structures of the world. In the CMC terminology, the knowledge of
concepts is _declarative_ knowledge - the knowledge of _what_. An example of
declarative knowledge is the final goal state of the tower-of-hanoi puzzle. In
contrast, procedural knowledge in tower-of-hanoi are the set of rules that
guide action selection in service of achieving the goal state. CMC
architectures agree that conceptual structures are useful for intelligent
behavior. To solve tower-of-hanoi, understanding the goal state is critical.
However, there is limited understanding of how declarative knowledge about the
world is acquired in CMC architectures. In this paper, we study the questions
of declarative concept representation, acquisition, and usage in task
performance in a prominent CMC architecture - Soar. As it is similar to ACT-R
and Sigma in the organization of computation and information, our findings can
be generalized to those architectures as well.

###  1.1 Declarative long-Term memories in Soar

In the past two decades, algorithmic research in Soar has augmented the
architecture with decalartive long-term memories (dLTMs). Soar has two -
semantic (Derbinsky et al., 2010) and episodic (Derbinsky and Laird, 2009) \-
that serve distinct cognitive functions following the hypotheses about
organization of memory in humans (Tulving and Craik, 2005). Semantic memory
enables enriching what is currently observed in the world with what is known
generally about it. For example, if a dog is observed in the environment, for
certain types of tasks it may be useful to elaborate that it is a type of a
mammal. Episodic memory gives an agent a personal history which can later be
recalled to establish reference to shared experience with a collaborator, to
aid in decision-making by predicting the outcome of possible courses of
action, to aid in reasoning by creating an internal model of the environment,
and by keeping track of progress on long-term goals. The history is also
useful in deliberate reflection about past events to improve behavior through
other types of learning such as reinforcement learning or explanation-based
learning. Using dLTMs in Soar agents has enable reasoning complexity that
wasn’t possible earlier (Xu and Laird, 2010; Mohan and Laird, 2014; Kirk and
Laird, 2014; Mininger and Laird, 2018).

However, a crucial question remains unanswered - how is general world
knowledge in semantic memory acquired? We posit that this knowledge is
acquired in two distinctive ways. Kirk and Laird (2014, 2019) explore the view
that semantic knowledge is acquired through interactive instruction when
natural language describes relevant declarative knowledge. An example concept
is the goal of tower-of-hanoi _a small block is on a medium block and a large
block is below the medium block._ Here, the trainer provides the definition of
the concept declaratively which is later operationalized so that it can be
applied to recognize the existence of a tower and in applying actions while
solving tower-of-hanoi. In this paper, we explore an alternative view that
that this knowledge is acquired through examples demonstrated as a part of
instruction. We augment Soar dLTMs with a new _concept memory_ that aims at
acquiring general knowledge about the world by collecting and analyzing
similar experiences, functionally bridging episodic and semantic memories.

###  1.2 Algorithms for analogical processing

To design the concept memory, we leverage the computational processes that
underlie analogical reasoning and generalization in the Companions cognitive
architecture - the Structure Mapping Engine (SME; Forbus et al. 2017) and the
Sequential Analogical Generalization Engine (SAGE; McLure et al. 2015).
Analogical matching, retrieval, and generalization is the foundation of the
Companions Cognitive architecture. In Why we are so smart?, Gentner claims
that what makes human cognition superior to other animals is “First,
relational concepts are critical to higher-order cognition, but relational
concepts are both non-obvious in initial learning and elusive in memory
retrieval. Second, analogy is the mechanism by which relational knowledge is
revealed. Third, language serves both to invite learning relational concepts
and to provide cognitive stability once they are learned” (Gentner, 2003).
Gentner’s observations provide a compelling case for exploring analogical
processing as a basis for concept learning. Our approach builds on the
analogical concept learning work done in Companions (Hinrichs and Forbus,
2017). Previous analogical learning work includes spatial prepositions
Lockwood (2009), spatial concepts (McLure et al., 2015), physical reasoning
problems (Klenk et al., 2011), and activity recognition (Chen et al., 2019).
This diversity of reasoning tasks motivates our use of analogical processing
to develop an architectural concept memory. Adding to this line of research,
our work shows that you can learn a variety of conceptual knowledge within a
single system. Furthermore, that such a system can be applied to not only
learn how to recognize the concepts but also acting on them in the environment
within an interactive task learning session.

###  1.3 Concept formation and its interaction with complex cognitive
phenomenon

Our design exploration of an architectural concept memory is motivated by the
interactive task learning problem (ITL; Gluck and Laird 2019) in embodied
agents. ITL agents rely on natural interaction modalities such as embodied
dialog to learn new tasks. Conceptual knowledge, language, and task
performance are inextricably tied - language is a medium through which
conceptual knowledge about the world is communicated and learned. Task
performance is aided by the conceptual knowledge about the world.
Consequently, embodied language processing (ELP) for ITL provides a set of
functional requirements that an architectural concept memory must address.
Embedding concept learning within the ITL and ELP contexts is a significant
step forward from previous explorations in concept formation. Prior approaches
have studied concept formation independently of how they will be used in a
complex cognitive system, often focusing on the problems of recognizing the
existence of a concept in input data and organizing concepts into a
similarity-based hierarchy. We study concept formation within the context of
higher-order cognitive phenomenon. We posit that concepts are learned through
interactions with an interactive trainer who structures a learner’s
experience. The input from the trainer help group concrete experiences
together and a generalization process distills common elements to form a
concept definition.

###  1.4 Theoretical Commitments, Claims, and Contributions

Our work is implemented in Soar and consequently, brings to bear the
theoretical postulates the architecture implements. More specifically, we
build upon the following theoretical commitments:

  1. 1.

Diverse representation of knowledge: In the past decade, the CMC architectures
have adopted the view that architectures for general intelligence implement
diverse methods for knowledge representation and reasoning. This view has been
very productive in not only studying an increasing variety of problems but
also in integrating advances in AI algorithmic research in the CMC framework.
We contribute to this view by exploring how algorithms for analogical
processing can be integrated into a CMC architecture.

  2. 2.

Deliberate access of conceptual knowledge: Following CMC architectures, we
assume that declarative, conceptual knowledge is accessed through deliberation
over when and how to use that knowledge. The architectures incorporates well-
defined interfaces i.e. _buffers_ in working memory that contain information
as well as an operation the declarative memory must execute on the
information. Upon reasoning, information may be stored, accessed, or projected
(described in further detail in Section 4).

  3. 3.

Impasse-driven processing and learning: Our approach leverages _impasse_ in
Soar, a meta-cognitive signal that can variably indicate uncertainty or
failure in reasoning. Our approach uses impasses (and the corresponding state
stack) to identify and pursue opportunities to learn.

  4. 4.

A benevolent interactive trainer: We assume existence of an intelligent
trainer that adopts a collaborative goal with the learning system that it
learns correct definitions of concepts. Upon being prompted, the trainer
provides correct information to the learner to base its concept learning upon.

Based on these theoretical commitments, our paper contributes an integrative
account of a complex cognitive phenomenon - interactive concept learning.
Specifically, this paper:

  1. 1.

defines the concept formation problem within larger cognitive phenomenon of
ELP and ITL;

  2. 2.

identifies a desiderata for an architectural concept memory;

  3. 3.

implements a concept memory for Soar agents using the models of analogical
processing;

  4. 4.

introduces a novel process - curriculum of guided participation - for
interactive concept learning;

  5. 5.

introduces a novel framework for evaluating interactive concept formation.

Our implementation is a functional (and not an architectural) integration of
analogical processing in Soar’s declarative long-term memory systems. It
characterizes how an analogical concept memory can be interfaced with the
current mechanisms. Through experiments and system demonstration, we show that
an analogical concept memory leads to competent behavior in ITL. It supports
learning of diverse types of concepts useful in ITL. Learned concept
representations support recognition during ELP as well as action based on
those concepts during task performance. The concepts are from few examples
provided interactively.

##  2 Preliminaries - The AILEEN Cognitive System

Aileen is a cognitive system that learns new concepts through interactive
experiences (linguistic and situational) with a trainer in a simulated world.
A system diagram is shown in Figure 1. Aileen lives in a simulated robotic
world built in Webots111https://www.cyberbotics.com/. The world contains a
table-top on which various simple objects can be placed. A simulated camera
above the table captures top-down visual information. Aileen is engaged in a
continuous _perceive-decide-act_ loop with the world. A trainer can set up a
scene in the simulated world by placing simple objects on the scene and
providing instructions to the agent. Aileen is designed in Soar which has been
integrated with a deep learning-based vision module and an analogical concept
memory. It is related to Rosie, a cognitive system that has demonstrated
interactive, flexible learning on a variety of tasks (Mohan et al., 2012,
2014; Mohan and Laird, 2014; Kirk and Laird, 2014; Mininger and Laird, 2018),
and implements a similar organization of knowledge.

Figure 1: System diagram for Advanced cognItive LEarning for Embodied
compreheNsion (Aileen)

#### Visual Module

The visual module processes the image taken from the simulated camera. It
produces output in two channels: object detections as bounding boxes whose
centroids are localized on the table-top and two perceptual symbols or
_percept_ s corresponding to the object’s shape and color each. The module is
built using a deep learning framework - You Only Look Once (YoLo: Redmon et
al. (2016)). YoLo is pre-trained with supervision from the ground truth in the
simulator ($12,000$ images). It is detects four shapes (error rate $<0.1\%$) -
_box_ (percept - CVBox), _cone_ (CVCone), _ball_ (CVSphere), and _cylinder_
(CVCylinder).

For colors, each detected region containing an object is cropped from the
image, and a $K$-means clustering is applied all color pixel values within the
crop. Next, two weighted heuristics are applied that selects the cluster that
likely comprises the detected shape among any background pixels and/or
neighboring objects. The first heuristic selects the cluster with the maximum
number of pixels. The second heuristic selects the cluster with the centroid
that is closest to the image center of the cropped region. The relative
weighted importance of each of these heuristics is then trained using a simple
grid search over $w_{1}$ and $w_{2}$: $Score=w_{1}R_{s}+w_{2}(1-C_{s}),s\in
D$, where $w_{1}+w_{2}=1$, $D$ is the set clusters, $R_{s}$ denotes the ratio
between the number of pixels in each cluster and the the number of pixels in
the image crop, and $C_{s}$ is the Euclidean distance between the centroid of
the cluster and the image center normalized by the cropped image width. The
average RGB value for all pixels included in the cluster with the highest
score is calculated and compared with the preset list of color values. The
color label associated with the color value that has the smallest Euclidean
distance to the average RGB value is selected. The module can recognize $5$
colors (error rate $<0.1\%$): CVGreen, CVBlue, CVRed, CVYellow, and CVPurple.
Note that the percepts are named so to be readable for system designers - the
agent does not rely on the percept symbol strings for any reasoning.

#### Spatial Processing Module

The spatial processing module uses QSRLib (Gatsoulis et al., 2016) to process
the bounding boxes and centroids generated by the visual module to generate a
qualitative description of the spatial configuration of objects. For every
pair of objects, the module extracts qualitative descriptions using two
spatial calculi (qsrs): cardinal direction (CDC) and region connection (RCC8).
Additionally, the spatial processing module can also convert a set of calculi
into regions and sample points from them. This enables Aileen to identify
locations in continuous space that satisfy qualitative spatial constraints
when planning actions.

#### World representation, Intrinsic & Extrinsic Behaviors

The outputs of the visual module and the spatial module are collected into an
object-oriented relational representation of the current state of the world.
Each detected object is asserted and represented with attributes that
indicated its color and shape visual properties and is assigned a unique
identifier. Qualitative relationships extracted by the spatial processing
module are represented as as binary relation between relevant objects. The set
of objects that exist on the scene and qualitative relationships between them
capture the current state of the world and are written to Soar’s working
memory graph.

Figure 2: (left) Simplified, partial working memory graph for the scene in
Figure 1. Green colored symbols are generated in the visual module and yellow
colored symbols are generated in the spatial module. Black colored symbols are
internal to Soar and are used for driving behavior. (right) Concepts in
semantic memory. Map nodes (M1, M2, M3, M4, M5, M6) connect words with their
conceptual definitions in semantic memory.

Interactive and learning behaviors in Aileen are driven by its procedural
knowledge encoded as rules in Soar and similarly to Rosie (Mohan et al., 2012)
consists of knowledge for:

  1. 1.

Interaction: As in Rosie (Mohan et al., 2012) Aileen implements collaborative
discourse theory (Rich et al., 2001) to manage its interactive behavior. It
captures the state of task-oriented interaction and is integrated with
comprehension, task execution, and learning.

  2. 2.

Comprehension: Aileen implements the Indexical Model of comprehension (Mohan
et al., 2014) to process language by grounding it in the world and domain
knowledge. This model formulates language understanding as a search process.
It interprets linguistic symbols and their associated semantics as cues to
search the current environment as well as domain knowledge. Formulating
language comprehension in this fashion integrates naturally with interaction
and learning where ambiguities and failures in the search process drive
interaction and learning.

  3. 3.

External task execution: Aileen has been programmed with primitive actions
that enable it to manipulate its environment: point(o), pick-up(o), and
place([x, y, z]). Following Mohan and Laird (2014), each primitive action has
a proposal rule that encodes its pre-conditions, a model that captures state
changes expected to occur when the action is applied, and an application rule.
Additionally, given a task goal, Aileen can use iterative-deepening search to
plan a sequence of primitive actions to achieve the goal and execute the task
in the world.

  4. 4.

Learning: Learning in Aileen is the focus of this paper and is significantly
different from Rosie. Rosie uses an interactive variation of explanation-based
learning (Mohan and Laird, 2014) to learn representation and execution of
tasks. Aileen uses analogical reasoning and generalization to learn diverse
concepts including those relevant to task performance (Sections 3 and 4). A
crucial distinction is that EBL requires a complete domain theory to correctly
generalize observed examples while analogical reasoning and generalization can
operate with partial domain theory by leveraging statistical information in
observed examples.

The ongoing ITL research in Soar demonstrates the strength of this
organization of knowledge in hybrid cognitive systems. Our conjecture is that
an ideal concept memory in an architecture must support complex, integrated,
intelligent behavior such as ELP and ITL.

#### Using Conceptual Knowledge in Aileen

Consider the world in Figure 1 and the corresponding working memory graph in
Figure 2. Semantic memory stores concept definitions corresponding to various
words used to interact with Aileen. _Maps_ (M1, M2, M3, M4, M5, M6) - in
semantic memory (shown in Figure 2) - associate words (_cylinder_) to their
conceptual definition (percept CVCylinder). Maps provide bi-directional access
to the association between words and concept definitions. The semantic memory
can be queried with a word to retrieve its concept definition. The semantic
memory can also we queried with a concept definition to access the word that
describes it.

Phrases (1) _blue cone left of red cylinder_ and (2) _move blue cone right of
red cylinder_ can be understood via indexical comprehension (details by as
follows:

  1. 1.

_Parse the linguistic input into semantic components_. Both (1) and (2) have
two references to objects: {or1: obj-ref{property:blue, property:cone}} and
{or2: obj-ref {property:red, property: cylinder}}. Additionally, (1) has a
reference to a spatial relationship: {rel1: {rel-name: left of, argument1:
or1, argument2: or2}}. (2) has a reference to an action: {act1: {act-name:
move, argument1: or1, argument2: or2, relation: left of} }. For this paper, we
assume that the knowledge for this step is pre-encoded.

  2. 2.

_Create a goal for grounding each reference_. The goal of processing an object
reference is to find a set of objects that satisfy the properties specified.
It starts with first resolving properties. The process queries semantic memory
for a percept that corresponds to various properties in the parse. If the
knowledge in Figure 2 is assumed, property blue resolves to percept CVBlue,
cone to CVCone, red to CVRed, and cylinder to CVCylinder. Using these
percepts, Aileen queries its scene to resolve object references. For or1, it
finds an object that has both CVBlue and CVCone in its description. Let or1
resolve to o1 and or2 to o2 where o1 and o1 are identifiers of objects visible
on the scene. The goal of processing a relation reference is to find a set of
spatial calculi that correspond to the name specified. If knowledge in Figure
2 is assumed, rel1 in (1) is resolved to a conjunction of qsrs
e(a1,a2)$\land$dc(a1,a2) i.e, object mapping to a1 should be east (in CDC) of
a2 and they should be disconnected. Similarly, act1 in (2) resolves to a task
goal which is a conjunction of qsrs w(a1,a2)$\land$dc(a1,a2)

  3. 3.

_Compose all references_ : Use semantic constraints to resolve the full input.
For (1) and (2) a1 is matched to to ar1 and consequently to o1. Similarly, a2
is resolved to o2 via ar2.

Tasks are represented in Aileen as goals that it must achieve in its
environment. Upon being asked to execute a task, _move blue cone right of red
cylinder_ , indexical comprehesion determines the desired goal state as
w(a1,a2)$\land$dc(a1,a2). Now, Aileen must execute a sequence of actions to
achieve this desired goal state in its environment. Leveraging standard pre-
conditions and effects of actions, Aileen can simulate the results of applying
plausible actions in any state. Through an iterative deepening search
conducted over actions, Aileen can generate and execute a plan that will
achieve a desired goal state in the environment.

##  3 The Interactive Concept Learning Problem

With an understanding of how indexical comprehension connects language with
perceptions and actions and how tasks are executed, we can begin to define the
concept learning problem. Our main question is this - where does the
conceptual knowledge in semantic memory (in Figure 2) come from? We study how
this knowledge is acquired through interactions with an intelligent trainer
who demonstrates relevant concepts by structuring the learner’s environment.
In Soar, episodic memory stores contextual experiences while the semantic
memory stores general, context-independent facts. Our approach uses
supervision from an intelligent trainer to group contextual experiences
together. An analogical generalization process distills the common elements in
grouped contextual experience. This process can be seen as mediating knowledge
in Soar’s episodic and semantic memories.

To develop our ideas further, we focus on learning three kinds of concepts.
These concepts are crucial for ELP and ITL. Visual concepts correspond to
perceptual attributes of objects and include colors and shapes. They provide
meaning to nouns and adjectives in the linguistic input. Spatial concepts
correspond to configuration of objects and provide grounding to prepositional
phrases in the linguistic input. Action concepts correspond to temporal
changes in object configurations and provide grounding to verb phrases.

###  3.1 A Curriculum of Guided Participation

We introduce a novel interactive process for training Aileen to recognize and
use novel concepts - _guided participation_. Guided participation sequences
and presents lessons - conjoint stimuli (world and language) - to Aileen. A
lesson consists of a scenario setup in Aileen’s world and an interaction with
Aileen. A scenario can be a static scene when training visual and spatial
concepts or a sequence of scenes when training an action concept. An
interaction has a linguistic component (_content_) and a non-linguistic
component (_signal_). The signal component of instruction guides reasoning in
Aileen and determines how it processes and responds to the content. Currently,
Aileen can interpret and process the following types of signals:

  1. 1.

inform: Aileen performs active learning. It uses all its available knowledge
to process the content through indexical comprehension (Section 3). If
failures occur, Aileen creates a learning goal for itself. In this goal, it
uses the current scenario to generate a concrete example of the concept
described in the content. This example is sent to its concept memory. If no
failure occurs, Aileen does not learn from the example. Aileen learning is
deliberate; it evaluates the applicability of its current knowledge in
processing the linguistic content. It learns only when the current knowledge
isn’t applicable, and consequently, Aileen accumulates the minimum number of
examples necessary to correctly comprehend the content.

  2. 2.

verify: Aileen analyzes the content through indexical comprehension and
determines if the content refers to specific objects, spatial relationships,
or actions in the accompanying scenario. If Aileen lacks knowledge to complete
verification, Aileen indicates a failure to the instructor.

  3. 3.

react: This signal is defined only when the linguistic content contains a
reference to an action. Aileen uses its knowledge to produce an action
instantiation. Upon instantiation, Aileen determines a goal state in the
environment and then plans, a sequence of actions to achieve the goal state.
This sequence of actions is executed in the environment.

Incorporating these variations in how Aileen responds to the linguistic
content in a lesson enables flexible interactive learning. A trainer can
evaluate the current state of knowledge in Aileen by assigning it verify and
react lessons. While the verify lesson tests if Aileen can recognize a concept
in the world, the react lesson tests if Aileen can use a known concept to
guide its own behavior in the environment. Observations of failures helps the
trainer in structuring inform lessons that guide Aileen’s learning. In an
inform lesson, Aileen evaluates its own learning and only adds examples when
necessary. Such learning strategy distributes the onus of learning between
both participants. Lessons can be structured in a flexible, reactive way in
real human-robot training scenarios.

Table 1: Predicate calculus representation for the world scene in Figure 1 corresponding to Soar’s working memory graph in Figure 2. CVCyl is short for the CVCylinder symbol and H for that holdsIn predicate that encodes which predicates hold in which episodic timepoint. Current world scene | Episodic trace
---|---
objects | relations | T0 | T1 | T2
(isa o1 CVBlue) | (e o1 o2) | (H T0 (dc o1 o2)) | (H T1 (held O1)) | (H T2 (w o1 o2))
(isa o1 CVCone) | (dc o1 o2) | (H T0 (e o1 o2)) | ... | ...
(isa o2 CVRed) | (w o2 o1) | ... | ... | (final T2 T1)
(isa o2 CVCyl) | (dc o2 o1) | (isa T0 start) | (after T1 T0) | (after T2 T1)

###  3.2 Desiderata for a Concept Memory

We extend the concept memory desiderata originally proposed by (Langley, 1987)
to enable embedding it within larger reasoning tasks, in this case ELP and
ITL:

  1. D0

Is (a) architecturally integrated and (b) uses relational representations.

  2. D1

Can represent and learn a diverse types of concepts. In particular, for
Aileen, the concept memory must be able to learn visual concepts, spatial
concepts, and action concepts.

  3. D2

Learn from exemplars acquired through experience in the environment. Aileen is
taught through lessons that have two stimuli - a scenario and linguistic
content that describes it.

  4. D3

Enable incremental accumulation of knowledge. Interactive learning is a
distinctive learning approach in which behavior is intertwined with learning.
It has been previously argued that interleaving behavior and learning splits
the onus of learning between the instructor and the learner such that the
instructor can observe the learner’s behavior and provide more
examples/instruction if necessary.

  5. D4

Learn from little supervision as realistically humans cannot provide a lot of
examples.

  6. D5

Facilitate diverse reasoning over definitions of concepts.

    1. (a)

Evaluate existence of a concept in the current environment, including its
typicality. This enables recognizing a concept in the environment.

    2. (b)

Envision a concept by instantiating it in the current environment. This
enables action in the environment.

    3. (c)

Evaluate the quality of concept definitions. This enables active learning - if
the quality of a concept is poor, more examples can be added to improve it.

##  4 Concept Memory

Concept learning in Aileen begins with a failure during indexical
comprehension in an inform lesson. Assume that Aileen does not know the
meaning of _red_ , i.e, it does not know that _red_ implies the percept CVRed
in the object description. When attempting to ground the phrase _red cylinder_
in our example, Indexical comprehension will fail when it tries to look-up the
meaning of the word _red_ in its semantic memory. As in Rosie, a failure (or
an impasse) in Aileen is an opportunity to learn. Learning occurs through
interactions with a novel concept memory in addition to Soar’s semantic
memory. Similarly to Soar’s dLTM, the concept memory is accessed by placing
commands in a working memory buffer (a specific sub-graph). The concept memory
interface has $4$ commands: create, store, query, and project. Of these, store
and query are common with other Soar dLTMs. create and project are novel and
explained in the following sections.

Table 2: Terms used in analogical processing, their definitions, and values in Aileen’s concept memory Term | Definition
---|---
Similarity |  The score representing the quality of an analogical match, degree of overlap
Correspondence |  A one-to-one alignment between the compared representations |
Candidate Inference |  Inferences resulting from the correspondences of the analogy |
Threshold |  Definition | Value
Assimilation |  Score required to include a new example into a generalization instead of storing it as an example | 0.01
Probability |  Only facts exceeding this value are considered part of the concept. | 0.6
Match |  Score required to consider that an inference is applicable in a given scene | 0.75

Aileen’s concept memory is built on two models of cognitive processes: SME
(Forbus et al., 2017) and SAGE (McLure et al., 2015) and can learn visual,
spatial, and action concepts (desiderata D0). Below we describe how each
function of concept memory is built with these models. The current
implementation of the memory represents knowledge as predicate calculus
statements or _facts_ , we have implemented methods that automatically
converts Soar’s object-oriented graph description to a list of facts when
needed. Example translations from Soar’s working memory graph to predicate
calculus statements are shown in Table 1. Visual and spatial learning requires
generating facts from the current scene. Examples for action learning are
provided through a demonstration which is automatically encoded in Soar’s
episodic memory. An episodic trace of facts is extracted from the episodic
memory (shown in Table 1). We will rely on examples in Table 1 for
illustrating the operation of the concept memory in the remainder of this
section. We have summarized various terms and parameters used in analogical
processing in Table 2.

###  4.1 Creation and Storage

When Aileen identifies a new concept in linguistic content (word _red_), it
creates a new symbol RRed. This new symbol in incorporated in a map in Soar’s
semantic memory and is passed on to the concept memory for creation of a new
concept via the create command. The concept memory creates a new reasoning
symbol as well as a new generalization context (shown in Figure 3). A
generalization context is an accumulation of concrete experiences with a
concept. Each generalization context is a set of individual examples and
generalizations.

Facts | P
---|---
(isa (GenEntFn 0 RRedMt) RRed) | 1.0
(isa (GenEntFn 0 RRedMt) CVRed) | 1.0
(isa (GenEntFn 0 RRedMt) CVCube) | 0.5
(isa (GenEntFn 0 RRedMt) CVCylinder) | 0.5

[table]

Figure 3: (left) SAGE maintains a generalization context for each concept. For
each example (circle) of a concept, it is either added to a generalization
(rounded rectangle) or maintained as an independent example for the concept.
(right) Facts and their probabilities in generalization context for RRed

After creating a new concept, Soar stores an example in the concept memory.
The command {store: [(isa o2 CVRed) (isa o2 CVCylinder) (isa o2 RRed)],
concept: RRed} stores that the object o2 in the world is an example of the
concept RRed. This example A is stored in the RRed generalization context as
is - as a set of facts. Assume that at a later time, Soar sends another
example B of RRed concept through the command {store: [(isa o3 CVRed) (isa o3
CVCube) (isa o3 RRed)], concept: RRed}. The concept memory adds the new
example to the RRed generalization context by these two computational steps:

  1. 1.

SME performs an analogical match between the two examples. The result of
analogical matching has two components: a correspondence set and a similarity
score. A correspondence set contains alignment of each fact in one example
with at most one fact from other. The similarity score indicates the degree of
overlap between the two representations. In the two examples A and B, there
are two corresponding facts: (isa o2 CVRed) aligns with (isa o3 CVRed) and
(isa o2 RRed) aligns with (isa o3 RRed). If the similarity score exceeds an
_assimilation threshold_ (Table 2), SAGE continues to the next step to create
a generalization.

  2. 2.

SAGE assimilates the two examples A and B into a generalization (e.g. Figure
3). It :

    1. (a)

Uses the correspondence to create abstract entities. In the two examples
provided, (isa o2 RRed) aligns with (isa o3 RRed) and (isa o2 CVRed) with (isa
o3 CVRed). Therefore, identifiers o2 and o3 can be replaced with an abstract
entity (GenEntFn 0 RRedMt).

    2. (b)

Maintains a probability that a fact belongs in the generalization. Because
(isa (GenEntFN 0 RRedMT) RRed) and (isa (GenEntFn 0 RRedMT) CVRed) are common
in both examples, they are assigned a probability of $1$. Other facts are not
in the correspondences and appear in $1$ of the $2$ examples in the
generalization resulting in a probability of $0.5$. Each time a new example is
added to this generalization, the probabilities will be updated the reflect
the number of examples for which the facts were aligned with each other.

Upon storage in a generalization context, a generalization becomes available
for matching and possible assimilation with future examples enabling
incremental (D3), example-driven (D2) learning.

###  4.2 Query

During indexical comprehension, Aileen evaluates if a known concept exists in
the current world through the query command. Assume that in an example scene
with two objects, indexical comprehension attempts to find the one that is
referred to by _red_ through {query: {scene: [(isa o4 CVRed) (isa o4 CVBox)
(isa o5 CVGreen) (isa o2 CVCylinder)], pattern: (isa ?o RRed)}}. In response
to this command, the concept memory evaluates if it has enough evidence in the
generalization context for RRed to infer (isa o2 RRed). The concept memory
performs this inference through the following computations.

  1. 1.

SME generates a set of candidate inferences. It matches the scene with the
generalization in Figure 3 (right). This match results in a correspondence
between the facts (isa o4 CVRed) in scene) and (isa (GenEntFn 0 RRedMt)
CVRed), which aligns o4 with (GenEntFn 0 RRedMt). Other facts that have
arguments that align, but are not in the correspondences, are added to the set
of candidate inferences. In our example, a candidate inference would be (isa
o4 RRed).

  2. 2.

AILEEN filters the candidate inferences based on the pattern in the query
command. It removes all inferences that do not fit the pattern. If the list
has an element, further support is calculated.

  3. 3.

AILEEN evaluates the support for inference by comparing the similarity score
of the match to the _match threshold_. That is, the more facts in the
generalization that participate in the analogical match then it is more likely
that the inference is valid.

Through queries to the concept memory and resultant analogical inferences, the
working memory graph (of the world in Figure 4) is enhanced. This enhanced
working memory graph supports indexical comprehension in Section 3. Note that
the internal concept symbols in blue (such as RBlue) are generalization
contexts in the concept memory that accumulate examples from training.
Consequently, the ‘meaning’ of the world _blue_ will evolve as more examples
are accumulated.

Figure 4: Working memory graph corresponding to scene in Figure 1 now enhanced
with concept symbols (blue). Each concept symbol refers to a generalization
context in the concept memory. The graph is enhanced based on inferences
supported by analogical processing.

(H (:skolem (GenEntFn 0 0 rMoveMt)) (held O1)
(after (:skolem (GenEntFn 0 0 rMoveMt)) T0)

Figure 5: Candidate inferences indicate that the next state of the move action
is to hold object O5. Skolem terms are generated by SME to indicate that the
candidate inference refers to an entity from the concept for which there is no
correspondence in the scene. In this case, the skolem represents the next
temporal state of the action as denoted by the after relation.

###  4.3 Projection

In ITL, simply recognizing that an action has been demonstrated is
insufficient, the agent must also be able to perform the action if directed
(desiderata D5). One of the advantages of analogical generalization is that
the same mechanism is used for recognition and projection. Consider the
example scene Figure 1 in which the trainer asks Aileen to _move the blue cone
to the right of the red cylinder_ using the react signal. Assume that Aileen
has previously seen some other examples of this action that are stored in
concept memory as episodic traces (an example is shown in 1).

During indexical comprehension, Aileen performs queries to identify the _blue
cone_ , O1, and _red cylinder_ , O2. Similarly, it maps the verb and the
related preposition to RMove and RRightOf. To act, Aileen uses its concept
memory to project the action through the command {project: {trace: [(H T0 (dc
o1 o2)) (H T0 (e o1 o2)) (isa AileenStartTime T0) ...], concept: RMove}. A
summary is shown in Figure 5 starting at T0. In response, the concept memory
performs the following computations:

  1. 1.

SME generates a set of candidate inferences. SME to matches the current scene
expressed as a trace against the generalization context of the action RMove.
SME generates all the candidate inferences that symbolically describe the next
states of the action concept.

  2. 2.

Aileen filters the candidate inferences to determine which apply in the
immediate next state (shown in Figure 5). For example, the trace in the
project command contains episode T0 as the AileenStartTime. The filter
computation will select facts that are expected to be held in (t) and that the
(after (t) T0) holds.

This retrieval is accepted by Aileen to be next desired state it must try to
achieve in the environment.

##  5 Evaluation

In this section, we evaluate how the proposed concept memory address the
desiderata outlined in section 3.2. As per desiderata D0, the concept memory
can be integrated into a CMC architecture through its interfaces (defined in
section 4) and SME & SAGE support inference and learning over relational
representations (in Table 1). For the remaining desiderata, we conducted a set
of empirical experiments and demonstrations.

  1. H1

As per D1, can the concept memory learn a diverse types of concepts? Our
hypothesis is that because SME & SAGE operate over relational, structured
representations, the concept memory designed with these algorithms can learn a
variety of concepts. We designed our experiments to study how Aileen learns
visual, spatial, and action concepts.

  2. H2

As per D2, D3, & D4, can the concepts be learned incrementally through
limited, situated experience? Aileen can learn from a curriculum of guided
participation that incrementally introduces a variety of concepts through a
conjoint stimuli of scene information with language. We designed our learning
experiments to reflect how a human-like teaching (Ramaraj et al., 2021) would
unfold and report our observations about the memory’s performance especially
focusing on the number of examples needed to learn from.

  3. H3

As per D5, does the concept memory support diverse reasoning? The
representations acquired by the concept memory not only support recognition of
a concept on the scene, it also guides action selection as well as identifying
opportunities to learn.

#### Method

We performed separate learning experiments for visual, spatial, and action
concepts (D1). We leverage the lessons of guided participation in the design
of our experimental trials. Each trial is a sequence of _inform_ lessons. In
an _inform_ lesson, a concept is randomly selected from a pre-determined set
and shown to Aileen accompanied with linguistic content describing the concept
(D2). The lesson is simplified, i.e, there are no distractor objects (examples
are shown in Figures 6, 7, & 8). The lesson is presented to Aileen and we
record the number of store requests it makes to the concept memory. Recall
that Aileen learns actively; i.e, it deliberately evaluates if it can
understand the linguistic content with its current knowledge and stores
examples only when necessary. The number of store requests made highlight the
impact of such active learning.

Additionally, to measure generality and correctness, we test Aileen knowledge
after every _inform_ lesson through two exams: generality and specificity
(examples are shown in Figures 6, 7, & 8). Both exams are made up of $5$
_verify_ lessons that are randomly selected at the beginning of the trial. As
Aileen learns, the scores on these test demonstrate how well Aileen can apply
what it has learned until now. In the generality lessons, Aileen is asked to
verify if the concept in the linguistic input exists on the scene. If Aileen
returns with a success status, it is given a score of $1$ and $0$ otherwise.
In the specificity exam, Aileen is asked to verify the existence of a concept,
however, the scenario does not contain the concept that is referred to in the
linguistic content. If Aileen returns with a failed status, it is given a
score of $1$ and $0$ otherwise. Both types of exam lessons have $0-3$
distractor objects introduced on the scene to evaluate if existence of noise
impacts the application of conceptual knowledge.

#### Results

Figure 6 illustrates visual concept learning. Aileen begins without any
knowledge of any concept. As two concepts (_green_ and _cone_) are introduced
in the first lesson, it provides several store commands to its concept memory
(shown in blue bars). The number of commands reduce as the training progresses
demonstrating that the learning is active and opportunistic (D5 c). As is
expected, the score on the generality exam is very low in the beginning
because Aileen doesn’t know any concepts. However, this score grows very
quickly with training eventually reaching perfect performance at lesson $15$.
The score on the specificity exam starts at $5$, this is to be expected as
well. This is because if a concept is unknown, Aileen cannot recognize it on
the scene. However, as the trial progress we see that this score doesn’t drop.
This indicates that conceptual knowledge of one concept doesn’t bleed into
others. Note that the exams have distractor objects while learning occurred
without any distractors - good scores on these exams demonstrate the strength
of relational representations implemented in Aileen. Finally, Aileen learns
from very few examples indicated that such learning systems can learn online
with human trainers (D3, D4).

Figure 6:  (left) Learning curve for visual concepts averaged from $10$
trials. A trial includes lessons from $5$ colors and $4$ and shapes $=20$
unique objects. Lessons include reference only to shape and color and shape.
(right) Examples of an _inform_ lesson (I) and generality (G) and specificity
(S) exam lessons. The blue bars show the average number of create or store
commands executed in the concept memory. The pink and green lines show average
score on the generality and specificity exams respectively.

Figure 7 illustrates spatial concept learning (commenced after all visual
concepts are already known). Spatial relationships are defined between two
objects each of which can be $1/20$ possible in the domain. Concrete examples
include irrelevant information (e.g., _left of_ ” does not depend on visual
properties of the objects). Despite this large complex learning space,
learning is quick and spatial concepts can be learned with few examples. These
results demonstrate the strength of analogical generalization over relational
representations. An interesting observation is that generality scores do not
converge to $5$ as in visual concept learning. A further analysis revealed
that in noisy scenes when the trainer places several distractors on the scene,
sometimes the objects move because they are placed too close and the
environment has physics built into it. The movement causes objects to move
from the intended configuration leading to apparent error in Aileen’s
performance. This is a problem with our experimental framework. The learning
itself is robust as demonstrated by number of store commands in the trial
which reduce to $0$ at the end.

Figure 7:  (left) Learning curve for spatial concepts averaged from $10$
trials. A trial includes lessons about $4$ types of binary relations defined
over $20$ unique objects. (right) Examples of an _inform_ lesson (I) and
generality (G) and specificity (S) exam lessons. The blue bars show the
average number of create or store commands executed in the concept memory. The
pink link shows average score on the generality exam and the green bar at the
top shows the average score on the specificity exam.

Figure 8 illustrates action learning (commenced after all visual and spatial
concepts have been learned). Actions are generated through the template _move
¡object reference 1¿ ¡relation¿ ¡object reference 2¿_. Similarly to spatial
concepts, the learning space is very large and complex. When Aileen asks, it
is provided a demonstration of action performance as shown in Figure 8 (T0,
T1, T2). Aileen stores the demonstration trace in its episodic memory. For
storing an example in the concept memory, information in Soar’s episodic
memory is translated into an episodic trace as shown Table 1. Similarly to
visual and spatial learning, inform lessons with simplified scene are used to
teach a concept. Exams made up of positive and negative verify lessons are
used to evaluate learning. As we see in Figure 8, Aileen can quickly learn
action concepts. Errors towards the later part of the experimental trial occur
for the same reason we identified in spatial learning.

Figure 8:  (left) Learning curve for action concepts averaged from $5$ trials.
A trial includes lessons about $1$ verb _move_ with $4$ different relations
and two objects chosen from $20$ unique objects. The blue bars show the
average number of create or store commands executed in the concept memory. The
pink link shows average score on the generality exam and the green bar at the
top shows the average score on the specificity exam. (right) A demonstration.
Figure 9: A simplified view of how Aileen plans a sequence of actions using
its concept memory. The process starts at the current state in T0 that is used
to generate a project command to the concept memory. The memory returns the
predicates to be achieved in the next state. An iterative deepening search
determines the action that will achieve it. This successive projection and
planning continues until the terminal state.

#### Task Demonstration

After visual, spatial, and action concepts were taught, we used a react lesson
to see if Aileen could perform the actions when asked. Consider the time T0 in
Figure 9 when Aileen is asked to _move the blue cone right of the red
cylinder_. It can successfully use methods of analogical processing to guide
action planning through the concept memory interface. First, it uses its
visual concepts during indexical comprehension to resolve _blue cone_ to (O1)
and the _red cylinder_ to (O2). It maps the verb _move_ to a known action
trace indexed by RMove. Then, it projects this action in the future. As
described in Section 4.3, the concept memory returns with a set of predicates
that have to be true in the next state (holds(O1)). Aileen plans using its
pre-encoded actions models and iterative deepening search. The search results
in pick-up(O1) where O1. After executing a pick-up action, Aileen invokes
projection again to determine if RMove requires more steps. In this case, it
does, and the candidate inferences specify that O1 should be located to the w
of O2 and they should be topologically disjoint. Further, these candidate
inferences indicate that this is the last step in the action, and therefore
Aileen marks the action as completed after executing it.

The symbolic actions generated through planning are incrementally transformed
into concrete information required to actuate the robot. pick-up executed on a
specific object can be directly executed using an inverse kinematics solver.
place action is accompanied with qualitative constraints. For example, to
place o1 to _right of_ o2, it must be place in a location that is to the west
and such that their bounding boxes are disconnected. Aileen uses QSRLib to
sample a point that satisfies the constraint. Once a point is identified, the
inverse kinematics solver can actuate the robot to achieve the specified
configuration. The successive projection and their interaction with action
planning is shown in Figure 9.

##  6 Related Work

Diverse disciplines in AI have proposed approaches for concept learning from
examples however, not all approaches can be integrated in a CMC architecture.
We use the desiderata defined in Section 3.2 to evaluate the utility of
various concept learning approaches. The vast majority study the problem in
isolation and consider only flat representations violating the desiderata D0.
ML-based classification approaches are designed for limited types of concepts
(such as object properties), violating desiderata D1, and require a large
number of examples, violating desiderata D4, which are added in batch-mode,
violating desiderata D3. On the other hand, while EBL and Inductive logic
programming (Muggleton and De Raedt, 1994) can learn from few datapoints, they
require fully-specified domain theory violating desiderata D2. Bayesian
concept learning Tenenbaum (1999) uses propositional representations,
violating D0, and each demonstration has focused on a single type of concept,
violating D1.

There are a few cognitive systems’ approaches to the concept learning problem
that aim toward the desiderata that we delineated in Section 3. In the late
$1980$s - early $1990$s, there was a concerted effort to align machine
learning and cognitive science around concept formation (Fisher, 1987). For
example, Labyrinth (Thompson and Langley, 1991) creates clusters of examples,
summary descriptions, and a hierarchical organization of concepts using a
sequence of structure examples. COBWeb3 (Fisher, 1987) incorporates numeric
attributes and provides a probabilistic definition differences between
concepts. Building off these ideas, Trestle (MacLellan et al., 2015) learns
concepts that include structural, relational, and numerical information. Our
work can be seen as a significant step in advancing these research efforts.
First, the proposed concept memory leverages the computational models of
analogical processing that have been shown to emulate analogical reasoning in
humans. Second, we place the concept learning problem within the larger
problems of ELP and ITL in a cognitive architecture context. We demonstrate
not only concept formation but also how learned concepts are applied for
recognition, scene understanding, and action reasoning. By integrating with
vision techniques, we demonstrate one way in which concept formation is tied
to sensing.

Another thread of work in the cognitive system’s community that we build upon
is that of analogical learning and problem-solving. Early analogical problem-
solving systems include Cascade (VanLehn et al., 1991), Prodigy (Veloso et
al., 1995), and Eureka (Jones and Langley, 2005). They typically used analogy
in two ways: (1) as analogical search control knowledge where previous
examples were used to guide the selection of which problem-solving operator to
apply at any time, and (2) for the application of example-specific operators
in new situations. Aileen differs in two important ways: (1) it relaxes the
need for explicit goals further in its use of projection to specify the next
subgoal of an action, and (2) it uses analogical generalization on top of
analogical learning to remove extraneous knowledge from the concept.

##  7 Discussion, Conclusions, and Future Work

In this paper, we explored the design and evaluation of a novel concept memory
for Soar (and other CMC cognitive architectures). The computations in the
memory use models of analogical processing - SAGE and SME. This memory can be
used to acquire new situated, concepts in interactive settings. The concepts
learned are not only useful in ELP and recognition but also in task execution.
While the results presented here are encouraging, the work described in this
paper is only a small first step towards an architectural concept memory. We
have only explored a functional integration of analogical processing in Soar.
The memory has not be integrated into the architecture but is a separate
module that Soar interacts with. There are significant differences between
representations that Soar employs and those in the memory. For an efficient
integration and a reactive performance that Soar has historically committed
to, several engineering enhancements have to be made.

There are several avenues for extending this work. We are looking at three
broad classes of research: disjunctive concepts, composable concepts, and
expanded mixed-initiative learning. Disjunctive concepts arise from homographs
(e.g., _bow_ in musical instrument versus _bow_ the part of a ship) as well as
when the spatial calculi does not align with the concept or the functional
aspects of the objects must be taken into account (e.g., a cup is _under_ a
teapot when it is under the spigot, while a saucer is _under_ a cup when it is
directly underneath). One of the promises of relational declarative
representations of the form learned here is that they are composable. This
isn’t fully exploited for learning actions with spatial relations in them. Our
approach ends up with different concepts for move-left and move-above. A
better solution would be to have these in the same generalization such that
Aileen would be able to respond to the command to _move cube below cylinder_
assuming it been taught a _move_ action previously along with the concepts for
_below_ , _cube_ , and _cylinder_. Another avenue is contextual application of
concepts. For example, _bigger box_ requires comparison between existing
objects. Finally a cognitive system should learn not only from a structured
curriculum designed by an instructor but also in a semi-supervised fashion
while performing tasks. In our context this means adding additional examples
to concepts when they were used as part of a successful execution. This also
means, when there are false positives that lead to incorrect execution,
revising the learned concepts based on this knowledge. One approach from
analogical generalization focuses on exploiting these near-misses with SAGE
(McLure et al., 2015).

Inducing general conceptual knowledge from observations is a crucial
capability of generally intelligent agents. The capability supports a variety
of intelligent behavior such as operation in partially observable scenarios
(where conceptual knowledge elaborates what is not seen), in language
understanding (including ELP), in commonsense reasoning, as well in task
execution. Analogical processing enables robust incremental induction from few
examples and has been demonstrated as a key cognitive capability in humans.
This paper explores how analogical processing can be integrated into the Soar
cognitive architecture which is capable of flexible and contextual decision
making and has been widely used to design complex intelligent agents. This
paper paves way for an exciting exploration of new kinds of intelligent
behavior enabled by analogical processing.

##  8 Acknowledgements

The work presented in this paper was supported in part by the DARPA GAILA
program under award number HR00111990056 and an AFOSR grant on Levels of
Learning, a sub-contract from University of Michigan (PI: John Laird,
FA9550-18-1-0180). The views, opinions and/or findings expressed are those of
the authors’ and should not be interpreted as representing the official views
or policies of the Department of Defense, AFOSR, or the U.S. Government.

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