Andoni Aranguren, Eneko Osaba, Silvia Urra-Uriarte, Patricia Molina-Costa
11institutetext: TECNALIA, Basque Research and Technology Alliance (BRTA),
Spain
11email<EMAIL_ADDRESS>22institutetext: Corresponding Author

# Age-Friendly Route Planner: Calculating Comfortable Routes for Senior
Citizens

Andoni Aranguren 11 Eneko Osaba 1122
Silvia Urra-Uriarte 11 Patricia Molina-Costa 11

###### Abstract

The application of routing algorithms to real-world situations is a widely
studied research topic. Despite this, routing algorithms and applications are
usually developed for a general purpose, meaning that certain groups, such as
ageing people, are often marginalized due to the broad approach of the
designed algorithms. This situation may pose a problem in cities which are
suffering a slow but progressive ageing of their populations. With this
motivation in mind, this paper focuses on describing our implemented Age-
Friendly Route Planner, whose goal is to improve the experience in the city
for senior citizens. In order to measure the age-friendliness of a route,
several variables have been deemed, such as the number of amenities along the
route, the amount of comfortable elements found, or the avoidance of sloppy
sections. In this paper, we describe one of the main features of the Age-
Friendly Route Planner: the preference-based routes, and we also demonstrate
how it can contribute to the creation of adapted friendly routes.

###### keywords:

Route Planning, Optimization, Smart Cities, Senior Citizens, Ageing People

##  1 Introduction

Nowadays, route planning is a hot topic for both urban planners and the
research community. The reason for this popularity can be broken down into two
factors. On the one hand, due to their complexity, it is a tough challenge to
solve this kind of problems. Hence, the inherent scientific appeal of these
problems is irrevocable. On the other hand, the business benefits of efficient
logistics and the social advantages that this would bring make addressing
these problems of great interest for companies and civil servants.

Evidence of this interest is the growing number of scientific publications
that are added to the literature year after year [1, 2, 3, 4]. It is also
interesting the growing number of open-source frameworks for route planning
that can be found in the community, which can be used to solve routing
problems of different kind. Examples of this kind of frameworks are Open Trip
Planner (OTP, [5]), Open-Source Routing Machine (OSRM, [6]), or GraphHopper.

Despite the large amount of research and developments made on the topic,
routing algorithms and applications are usually developed for a general
purpose, meaning that certain groups with mobility restrictions, such as
ageing people, are often marginalised due to the broad approach of their
designs. In most cases, routing algorithms aim to optimise efficiency factors,
such as the traverse speed, distance, and public transport transfers. However,
these factors can result in a route that is challenging for underrepresented
groups with specific physical needs such as periodic resting, hydration to
prevent heat strokes and incontinence. These groups, typically older people
and people with with physical disabilities/ physical limitations, require
route planners with a different approach that integrates accessibility
factors.

In line with this, many European cities are experiencing a slow but
progressive ageing of their populations, arising a considerable spectrum of
new concerns that should be taken into account. For this reason, and as a
result of these concerns, policy makers and urban planners are in a constant
search of novel initiatives and interventions for enhancing the participation
in the city life of senior citizens.

In this context, Artificial Intelligence (AI) has emerged as a promising field
of knowledge area for dealing with ageing people related concerns. For this
reason, a significant number of municipalities and cities have adopted AI
solutions into their daily activity, implementing various systems for
constructing innovative functionalities around, for example, mobility.
However, the potential of AI for the development of innovative age-friendly
functionalities remains almost unexplored, for example the development of age-
friendly route planners. Few efforts have been made in this direction, such as
the work recently published in [7] describing a preliminary prototype is
described for planning public transportation trips for senior citizens.

With this motivation in mind, the main objective of this research is to
present our developed Age-Friendly Route Planner, which is fully devoted to
providing senior citizens with the friendliest routes. The goal of these
routes is to improve the experience in the city for these ageing users. In
order to measure this friendliness, several variables are considered, such as
the number of amenities along the route, the number of elements that improve
the comfortability of the user, or the consideration of flat streets instead
of sloppy sections.

Specifically, in this paper we detail one of the main functionalities of our
Age-Friendly Route Planner: the preference-based route planning. Thanks to
this functionality, adapted walking routes can be computed based on four
weighted preferences inputted by the user and related to i) the duration, ii)
the incline of the streets traversed, iii) the amount of amenities found
throughout the route, and iv) the overall comfortability of the trip. The
entire route planner has been implemented based on the well-known Open Trip
Planner111 https://www.opentripplanner.org/.

To properly develop this functionality, different real-world data have been
used, and two ad-hoc data-processing engines have been implemented, namely,
the Standardized Open Street Maps Enrichment Tool (SOET), and the Amenity
Projection Tool (AOT). These tools, along with the preference-based route
planning functionality, and the overall structure of the Age-Friendly Route
Planner are described in detail along this paper. In addition, we show some
solution examples in the city of Santander, Spain, to demonstrate the
applicability of the planner we have developed.

It should be pointed out here that the preference-based route planning
functionality supposes a significant innovation for our Age-Friendly Route
Planner regarding the vast majority of general-purpose route planners
available in the literature, which do not compute this type of age-friendly
routes. SOET and AOT also represent a remarkable contribution to this work, as
they can be easily replicated in other route planners and Open Street Map
(OSM, [8]) based applications.

The structure of this paper is as follows. In the following Section 2, we
detail the overall structure of the Age-Friendly Route Planner. In Section 3,
we describe the main data used by the planner to properly perform the
functions that this paper focuses on. We also describe SOET and AOT in this
section. In addition, in Section 4, we describe the preference-based route
planning. In this section, we also introduce some examples of its
applicability. Lastly, we finish this paper with conclusions and further work
( Section 5).

##  2 The Age-Friendly Route Planner

After analysing a significant amount of the most popular open source route
planners (such as GraphHopper, OptaTrip, Traccar, OSRM or MapoTempo, among
many others), we found that most of them are mainly designed for vehicle
routing. This fact highlights the need for a solution that is primarily aimed
at citizens. With this in mind, and as mentioned in the introduction, OTP has
been selected as the framework for being used in this work. This fact does not
imply that the advantages of other alternatives should be underestimated, but
the main features, flexibility, and benefits that OTP offers to developers led
us to choose it as an excellent platform for achieving the main objectives
established. On closer examination, several important reasons led us to choose
OTP as the base framework, which are the following:

  * •

It is fully open source, meaning that it can be fully customised to fulfill
the research requirements.

  * •

It works efficiently with widely known standards such as OSM or Geotiff (for
defining city elevations).

  * •

Being published in 2009, OTP is a platform with a long trajectory. Therefore
it is very well documented and has a large and active community working on it.
This facilitates the understanding of the framework.

  * •

Both the API and the outcome JSON are fully customisable to the research
requirements.

As for the main structure of the Age-Friendly Route Planner, it has a central
module coined as route planning module, which is responsible for calculating
routes using both the available data and the information entered by the user
via API as input. In Figure 1 we represent the overall architecture of the
Age-Friendly Route Planner, considering also the data needed for its correct
use and the ad-hoc tools implemented for gathering the correct data.

---
Figure 1: Overall architecture of the Age-Friendly route planner.

Having said that, in order to properly contemplate all the requirements that
the routing system should fulfill, the data sources described in the following
section have been used. All these data sources are embedded in the OTP
platform so that they can be taken into account in the correct planning of the
routes.

##  3 Data Sources and Data Processing Engines

In order to generate the required routes appropriately, the Age-Friendly Route
Planner must build the corresponding street network. To do this, we need the
corresponding OSM map file of the city in question; in the case of this study,
Santander, Spain. The OSM format is fully compatible with OTP, which
automatically consumes the files and builds the corresponding road network.

In addition, this OSM file also takes into account important elements for the
planner such as elevators, benches, fountains, toilets and automatic ramps. In
line with this, it should be highlighted that the OSM files that can be openly
obtained from open platforms are usually not as specific as we need them to be
for our research. Open OSM files have proven to be very efficient for routing,
but in terms of amenity related content, they are far from meeting the needs
of this specific research. For this reason, we have developed an ad-hoc tool
for enriching the Standardized OSM files. We have coined this tool as
Standardized OSM Enrichment Tool, or SOET.

###  3.1 Standardized OSM Enhanced Tool - SOET

As explained earlier, for the Age-Friendly Route Planner, it is necessary to
contemplate the amenities that are spread across the city, as this is a
crucial factor for the success of the route planner. For this reason, the
Santander’s City Council, provided us with a series of files containing a list
of amenities with the corresponding geospatial data. Among these amenities, we
found benches, drinkable water sources, handrails and toilets. As the data was
not provided in OSM format, it had to be pre-processed in order to be
consumed. SOET was developed out of this motivation.

A data enrichment solution has been chosen over other options for this
purpose, as data enrichment is in itself a crucial functionality for any
project. Furthermore, as OSM is the basis for map creation in OTP, SOET
provides a cascading effect for all applications based on the OSM file. Some
examples are data visualisation in OTP and also the APT described later.

The required files for the Python-based SOET are the OSM file in which the
data is stored and a .csv file containing the amenities to be loaded. In the
following Figure 2 we can see a clear example of an OSM file before enrichment
(Figure 2.a) and the result of applying SOET (Figure 2.b). In this figure, we
can see the newly added elements: Benches (pink dots), drinking fountains
(blue dots) and garbage cans (red dots).

Figure 2: A map excerpt of Santander before (a) and after (b) the SOET
application.

###  3.2 Amenity Projection Tool - APT

At this moment, four different types of amenities have been deemed in the
route: public toilets, benches, handrails and drinking fountains. All these
amenities have been extracted from the OSM maps. For this purpose, a new tool
has been developed as part of this research. We have coined this tool as
Amenity Projection Tool, or APT.

The APT was developed to correlate street segments (ways) and amenities
(nodes). Thus, the AOT starts by reading the OSM file and then creates a
bounding box that encloses each way. This bounding box is loose enough to
enclose nearby nodes, and it is larger than the minimum bounding box by a
user-defined maximum distance. So the bounding box is created to reduce the
amount of correlation calculation which was $O(A_{t}*W_{t})$ , where $A_{t}$
is the total amount of amenities and $W_{t}$ is the total number of ways. It
reduces it to the bare minimum by adding a $O(W_{t})$ pre-process resulting in
a $O(A_{p}*W_{p}+A_{t})$ = $O(A_{p}*W_{p})$, where $A_{p}$ and $W_{p}$ are a
subset of amenities and ways which are also less than or equal to $A_{t}$ and
$W_{t}$. This approach has been chosen to ensure that the algorithm is
efficient. In Figure 3.a we represent this situation graphically, considering
that the green amenities are close enough to the street, in order to correlate
these amenities with the road. After this first step, each amenity node
(point) within the way bounding box is projected with an orthographic
projection onto each path segment (lines). Several measures can be extracted
from this projection, but only one is currently considered: the distance
between the point and its projection. This distance indicates how far the
amenity is from the segment. If it is equal to or less than the specified
maximum distance parameter, then the amenity is added to its amenity type
count. Figure 3.b represents this situation.

(a) APT criteria for considering amenities

(b) How APT relates amenities and ways

Figure 3: Basic concepts of APT

After the execution of these two phases, all correlations among ways and
amenities are stored in a .csv which contains the identifications of all the
ways and the number of amenities of each type that are within the
parameterised maximum distance from that way.

###  3.3 Elevation Data

In order to calculate friendly routes for senior citizens, the Age-Friendly
route planner also considers the incline of the streets. This is a crucial
aspect deeming that steep streets are usually preferred to be avoided and
sometimes could create unwalkable routes for the older people. Consequently, a
file containing the elevation of the city is compulsory. Luckily, OTP already
allows the consumption of this information using the widely known GeoTIFF
metadata standard. GeoTIFF permits the georeferenced of different information
embedded into a .tif file. With such a file, OTP can assign to a certain
elevation to its corresponding street. For this purpose, the .tif file has
been obtained from the SRTM 90m Digital Elevation Database open
platform222https://cgiarcsi.community/data/srtm-90m-digital-elevation-
database-v4-1/.

##  4 Building Preference-based routes in the Age-Friendly Route Planner

In order to calculate routes based on user preferences, a functionality coined
as Square Optimization has been implemented in the Age-Friendly Route Planner.
This kind of optimization allows the user to define four different preferences
for the calculation of walking routes (whose sum must equal 100%).

  * •

Slope: this factor regards the incline of the route. The higher this factor,
the flatter the routes calculated by the planner. The streets incline is
calculated using the elevation data described in Section 3.3.

  * •

Duration: this factor regards the duration of the route. The higher this
factor, the shorter the routes calculated in terms of time.

  * •

Amenities found along the route: this factor considers the amenities described
in the previous Section 3.2. In this case: benches, toilets and drinking water
fountains. The higher this factor, the more amenities will be found along the
route. In other words, a high value of this factor implies that the route
planner will prioritize going through streets that contain these amenities.

  * •

Comfortability factor: the comfortability factor considers those elements that
make the route more comfortable for the user. At the time of writing this
paper, and because of a lack of additional data, only handrails have been
included in this comfortability factor. In future stages of the Age-Friendly
route planner, additional aspects such as shadows will be contemplated for
this factor. Just like the amenities factor, the higher the comfortably
factor, the more comfortable the routes will be.

In order to demonstrate the applicability of this kind of routes, a testing
purpose webpage has been deployed based on OTP. This page is fully accessible
to any interested
reader333https://afrp.santander.urbanage.digital.tecnalia.dev/. In here the
user is able to introduce their preferences using the interactive interface.
We show in Figure 4 two examples for this preference settings. Also, in this
webpage, the user is able to choose routing options such as the origin and
destination of the path.

Figure 4: Visual examples of the Square Optimization in the Age-Friendly route
planner for walking routes.

Now we represent four different examples of walking routes, each one clearly
prioritizing one of the deemed factors. As explained before, this
demonstration is placed in the city of Santander, Spain; and in order to
properly show the impact of the four factors, same origins and destinations
are considered for each route. Thus, Figure 5.a represents a route which
prioritizes quickness. Secondly, Figure 5.b depicts a path devoted to use low-
incline routes. Meanwhile, Figure 5.c shows a route prioritizing the
appearance of amenities along the route. Lastly, Figure 5.d represents a path
which prioritizes comfortability, which means that it has handrails. Moreover,
we depict in Table 1 a summary of the features of each route, in order to
clearly visualize the main characteristics of each path, and how the choosing
of different preferences is crucial for building adapted routes.

(a) A preference-based route prioritizing quickness.

(b) A preference-based route prioritizing low-slope streets.

(c) A preference-based route prioritizing the finding of amenities.

(d) A preference-based route prioritizing the comfortability of the route

Figure 5: Different examples demonstration the application of the preference-
based walking routes functionality of the Age-Friendly Route Planner.

| Route I-Fig 5.a | Route II-Fig 5.b | Route III-Fig 5.c | Route IV-Fig 5.d
---|---|---|---|---
| Preferences of the route
Slope Factor | 14% | 74% | 4% | 8%
Duration Factor | 72% | 8% | 15% | 3%
Amenity Factor | 12% | 2% | 66% | 22%
Comfortability Factor | 2% | 16% | 15% | 67%
| Information of the route
Incline | 487,3 | 447,3 | 504,5 | 514,5
Duration | 34min | 38min | 37min | 38min
Amenities | 101 | 88 | 161 | 120
Comfortable Elements | 40 | 46 | 60 | 65

Table 1: Parameters and information about the routes calculated. Incline: sums
the overall elevation-comfortability of the route (the less the better).
Duration: duration of the route. Amenities: number of amenities found.
Comfortable: amount of comfortable elements.

##  5 Conclusions

The application of routing algorithm to real-world situations has been a hot
research topic in the last decades. As a result of this interest, the research
carried out in this field is abundant. Despite this, routing algorithms and
applications are usually developed for a general purpose, meaning that certain
groups, such as ageing people, are often marginalized because of the broad
approach of the designed algorithms. This situation may pose a problem in
different parts of the world, such as Europe, in which many are experiencing a
slow but progressive ageing on their populations, arising a considerable
spectrum of new challenges and concerns that should be approached.

With this motivation in mind, this paper is focused in describing our own
routing solution called Age-Friendly Route Planner. This planner is fully
devoted to providing ageing citizens with the friendliest routes. The main
objective of this route planner is to improve the experience in the city for
senior people. To measure this friendliness, several variables have been taken
into account, such as the number of amenities found along the route, the
number of elements that improve the comfortability of the user along the path,
the usage of urban infrastructures or avoiding sloppy sections.

Having shown and demonstrated one of the main functionalities of the Age-
Friendly Route Planner, which is the preference-based route planning, several
research lines have been planned as future work. As a short term, we will
implement further features on our route planner, such as in public
transportation routes. or the consideration of people using wheelchair. As a
long-term activity, we have planned to extend our Age-Friendly Route Planner
to other European cities which might arise unique challenges.

## Acknowledgments

This work has received funding from the European Union’s H-2020 research and
innovation programme under grant agreement No 101004590 (URBANAGE).

## References

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machine needs to subdivide these path, to determine what is the path to the
reward, what is the path to punishment. This segmentation process is to add
more Tokens to this path, so as to form multiple segmentation paths (such as
different scenarios, or different time points, or different factors, etc.), so
that the machine can determine its response through the segmentation path,
which is the core of segmentation imitation.

So, our machine does not need to accommodate the new "Fine tuning". It simply
needs to achieve "Fine tuning" by accumulating memories. It can perform a
"Fine tuning" at any depth, a "Fine tuning" in any field, and a superimposed
"Fine tuning" in countless fields without "catastrophic forgetting". This is
because it does not modify the past knowledge parameters, but rather does
simply amplify the network.

In this case, the hypothesis is that during the day, the machine is lying
(saving some power, getting rewards), after activating the punishment symbol
of the owner’s voice print, the machine needs to avoid the probability of the
activated penalty symbol and increase the probability of the activated reward
symbol. So, there are at least two Cases, 1, to reduce the probability of the
"lying" concept and avoid punishment (such as being reprimanded); 2, to
increase the probability of the "lying" concept and get a reward (such as
saving power). At this time, the machine needs to make the best choice
according to the principle of seeking advantages and avoiding disadvantages.
At this time, the machine has to synthesize various response paths, and
compare the statistical rewards and penalties.

If the machine is fully powered, the reward for saving power is small. After
completing the chain-type associative activation process, only one penalty
symbol achieved a high activation value. The machine will choose to avoid the
punishment, because the highest reward value. So the machine, driven by profit
maximization, will avoid punishment as a goal and start to build a response.

Assuming the machine is running low, the power savings is significant
(assuming the machine has to lie down and charge). After completing the chain-
type associative activation process, a penalty symbol obtains a high
activation value, and a reward symbol also obtains a high activation value.
According to the principle of seeking benefits and avoiding disadvantages, the
machine simultaneously establishes two goals: to achieve rewards and avoid
punishment. Because this is the highest reward value. So the machine, driven
by profit maximization, will take the reward + avoid punishment as the goal
and start to build a response.

Assuming the machine is fully powered, the machine now creates a level 2 goal:
reducing the activation value of the "lying down" concept. Therefore, under
the constraint of the level 2 objective, the machine looks for the propagation
path to transfer the activation value to the "lying" concept, and creates the
level 3 objective: reducing the activation value of the concepts on the
propagation path. Thus, the machine finds that the main path to propagate the
activation value to the concept of "lying" is the input of a set of self-state
sensors. So the machine creates a level 3 target: reducing the probability of
these input Tokens.

The machine will record various internal and external parameters of each
training, using memory and forgetting mechanism, encourage it to imitate the
reward parameters and avoid the reward parameters. In this way, an empirical
connection is established between parameter combination, reward and internal
and external environment. This is essentially a reinforcement learning
process. Of course, humans can also imitate its form, implant innate knowledge
(drive related) into the machine, or use the accumulated human experience to
directly modify the knowledge of the machine so that it converges as soon as
possible. So in different environments, the environment Tokens will
automatically activate the most relevant memories, by imitating these
experiences, passing similar combinations of parameters to the machine’s motor
system (including parameter types and their temporal order, these processes
are all automatically completed). This allows the machine to stand up in
various environments, reducing the probability of "lying" related Tokens.

Assuming that the machine is then low, the machine’s experience in achieving
rewards will allow it to lie down, increasing the probability of charging
related Tokens implementation. The experience of avoiding punishment, it
imitates past experience and explains to the owner why he is doing so. The
machine then creates a level 2 objective: raising the activation value
achieved by the "charge" concept. To ic the past experience of avoiding
"punishment". So the machine may create level 3 goal: "to explain the reason
of their behavior", because the "Tokens combination" in memory, and "avoid
punishment" Tokens combination there are close relationship between activation
value transfer, so the goal of the machine is to improve and specific Tokens
combination (to explain their behavior) occurrence probability. So the next
level of decision of the Tokens combination is: the experience of language
organization is activated.

This process proceeds iteratively, and each time a new reward and penalty
symbol is activated. The machine counts the activation values of these reward
and penalty symbols until the activation values of the reward and penalty
symbols converge. Then the machine establishes the optimal response path.

The machine then goes into the imitation execution process. The decision path
of the machine needs to be decomposed iteratively up to the underlying drive
parameters, so that it can issue the drive command by imitating the parameter
configuration in experience to imitate the execution.

In practice, experience and reality can always only be partially matched, so
the generalization between experience and reality can only be realized by
imitating their common Tokens combination mode.

Among these pathways, those composed of high activation value Tokens are the
top-level imitation pathways. If the imitation path does not contain a direct
underlying driver command combination, then more Tokens (lower activation
value Tokens) is added in, and then the imitation path becomes a different
combination of multisegment paths formed by more Tokens. This is what
segmented imitation means.

That is to say, when we face a large path without the appropriate experience,
we can then refine it and decompose it into several small response path
segments. For each small path segment, we can look for the right experience to
generalize the experience. If it is still impossible to decompose to the
direct underlying driver command combination, then repeat the process by
adding more Tokens, decompose the response path into more small path segments,
and then find the right experience to generalize the experience. If you still
can’t break down to the direct underlying driver command combination, then
repeat the process until it breaks down into the direct underlying driver
command combination.

The above process continues iteratively. There may be new Tokens input all the
time. Whenever a new Tokens input, the machine needs to do the chain
association activation process again. After completion, the distribution of
activation values in the memory bank changes, so the machine needs to restart
the decision-making process. So in this process, the optimal decision of the
machine may be to put down some of the current goals and start to pursue the
latest goals.

So, our machine produces its own goals and can constantly change its own
goals, so its decisions are very flexible and match the environment.

So in the example above, the possible result of the machine is to stand up
immediately, improve the resolution of the sound processing system, and turn
around to observe the owner’s posture, movement and expression, but until this
moment, the owner may have just said " Hello…"Word, the latter words have not
yet begun.

So, our machine is human-like intelligence, and its understanding of
information comes from its own experience, not from the statistical process.
Only in this way can our machines have personalized services.

A thousand housewives, with a thousand different requirements. The artificial
intelligence obtained through the knowledge statistics, the robot that cannot
update the knowledge in real time, can never enter the home, and can never
enter the hearts of the housewives. Their landing scenarios will be very
limited, and our solution, which is the real general artificial intelligence,
and it may change the face of the world.

##  9 Conclusion

We believe that the development of artificial intelligence can be
approximately divided into different stages: (1) the "feature exploration"
stage. Before deep learning, it was mainly focused on the "manual exploration"
stage. After deep learning, focus on the "machine exploration" phase.(2) After
the realization of real attention (Transformer), the machine realizes the
"knowledge generalization" after the initial alignment of the machine’s
"knowledge coordinate base cluster" and the human "knowledge coordinate base
cluster (concept)". In the face of human tasks, machines can show certain
intelligence through "knowledge generalization".[29][30]

The one-dimensional attention mechanism brings about a large model of
language. Two-dimensional attention mechanism brings about image
generalization. Three-dimensional attention mechanism, can achieve 3D creative
ability. The four-dimensional (3 D + time) attention mechanism can realize the
generalization of dynamic processes: it will bring video generation and robot
services in limited scenes.

But we believe that only by increasing the "vitality: the fifth dimension,
self-demand", can we bring the real "soul" to the machine intelligence. And
the big model doomed it to achieve the "fifth dimension". And our solution can
give "life" to the machine, so it can become a true "universal artificial
intelligence".

So, we believe that AI needs to move on to the next stage: the "autonomous
interaction" stage."Autonomy" means that the machine is no longer a silent
"machine", it can spontaneously produce behavior (which is equivalent to
programming itself), and the machine explores knowledge (for example, actively
interacting with the environment to acquire knowledge)."Interaction" means
that the machine can interact with the environment in real time, update its
knowledge in real time, and make continuous decisions to complete complex
tasks in an unfamiliar environment[29].

Many famous scholars have put forward their own views on how to move to the
real general artificial intelligence, for example, Professor Lecun proposed
the "world model", Professor Zhu Songchun also proposed the four
characteristics of general artificial intelligence:

(1) can perform unlimited tasks;

(2) can independently generate new tasks;

(3) valuable system driven;

(4) has a world model reflecting the real world.

Obviously, our plan is a response to the ideas of Professor Lecun and
Professor Zhu Songchun.

General artificial intelligence is the original intention of artificial
intelligence, but also the crown of artificial intelligence. We present a set
of technical solutions for implementing general AI, including implementation
steps with Step by Step. In reference [25][26][27][28], we reveal in detail
the technical steps to achieve this path by patent form. It may be the right
path to lead humanity to general artificial intelligence.

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