Artificial Consciousness, Artificial Emotions, And Autonomous Robots, Alan Cardon
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Abstract Nowadays for robots, the notion of behav-
ior is reduced to a simple factual concept at the level of the movements. On another hand, consciousness is a
very cultural concept, founding the main property of
human beings, according to themselves. We propose to
develop a computable transposition of the conscious-
ness concepts into artificial brains, able to express
emotions and consciousness facts. The production of
such artificial brains allows the intentional and really
adaptive behavior for the autonomous robots. Such a
system managing the robot’s behavior will be made of
two parts: the first one computes and generates, in a
constructivist manner, a representation for the robot
moving in its environment, and using symbols andconcepts. The other part achieves the representation of
the previous one using morphologies in a dynamic
geometrical way. The robot’s body will be seen for it-
self as the morphologic apprehension of its material
substrata. The model goes strictly by the notion of
massive multi-agent’s organizations with a morpho-
logic control.
Keywords Artificial consciousness Intentionality
Representation Organizational memory Artificial
emotions Embodiment Multi-agent systems
Morphology
Introduction
We deal with the notion of artificial consciousness
facts, i.e., the facts that living beings endowed with
brain are able to generate in their mind about the
things of the world they can conceive. The problem is
neither about the performance nor the qualities of the
faculty to thought about things, but it is about the
fundamental ability to make here and now a repre-
sentation about the things we manipulate in an abstract
but intelligible manner in mind.
By analogy with the notion of consciousness for a
long time invested by philosophers, psychologists and
neurobiologists, we will pose the question of the artifi-cial consciousness strictly in a constructivist way: how
can one transpose the fact of ‘‘thinking to something’’
into the computable field, so that an artificial system,
founded on computer processes, would be able to
generate consciousness facts, in a viewable manner for
us, the interested observers. The problem is to find the
good approach and the good level for the production of
a realistic model. On the reality of the life we have, on
one hand, a neural network made of very numerous
cells. We have, on the other hand, our mind and the
impression we can have about this component of our-
selves as generating sophisticated representations
about things of the world. How to transpose into the
computable field? The fact it is possible, for the brains,
to think about things, in what way an artificial system
can generate an intelligible representation of things and
facts, that will be the state of this system considered as
having intentions, emotions, ideas by the way of things
and events concerning itself? This system necessarily
must be linked to a body that it directs, but whose
constraints must it respect? But it also must have
Communicated by Ge rard Sabah.
A. Cardon (&)LIP6, Laboratoire d’informatique de Paris VI, UPMC Case169, 4 Place Jussieu, 75252 Paris Cedex 05, Francee-mail: [email protected]
Cogn Process (2006) 7:245–267
DOI 10.1007/s10339-006-0154-7
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RE S E A RC H RE PO RT
Artificial consciousness, artificial emotions, and autonomousrobots
Alain Cardon
Received: 21 January 2006 / Revised: 24 August 2006/ Accepted: 8 September 2006 / Published online: 3 October 2006 Marta Olivetti Belardinelli and Springer-Verlag 2006
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‘‘artificial’’ real life experiences, intentions to act and to
think, and must have knowledge and notably a knowl-
edge using words of language, it must have emotions
and intentions, and finally it must be conscious of itself.
We shall call such a system, by analogy with brain, an
artificial brain but we shall see that its architecture is
strong moved away of the one of the brains: it is going
to transpose effects, movements, and not replicates, thebrain constituents like the neurons or gliale cells.
We will keep in mind principally one characteristic of
the process of thinking unfolding in the brain: there is a
complex neural, biochemical, and electrical activation
movement. This movement is coupled, but with a dif-
ferent period, a similar one in the body nervous system.
This very complex movement generates, by selective
emergence reaching a particular configuration, what we
call a thought about something. This thought rapidly
leads to actuators or language activity and descends
then in the next thought, which can be similar or dif-
ferent. This is the very complex phenomenon that wehave to transpose in the computable domain.
Hence, we will approach the sudden appearance of
thoughts in brains at the level of the complex dynamics
of a system building and reconfigure recurrent and
temporized flux. We transpose this into computer
processes architectures containing symbolic meaning
and we will make it geometrically self-controlled. Two
reasonable hypotheses are made for this transposition:
• Analogy between the geometrical dynamics of the
real brain and the one of the artificial brain: in the
first case, flows are complex continuous forms andfor the other they are dynamic graphs whose
deformations are evaluated in a topological manner,
• Combinatory complexity reduction of the real brain
in the computable domain by using symbolic and
pre-language level for this approach. The basic
elements are completely different; they are not of
the same scale.
However, once these hypotheses made, one should
not start to develop an architecture that will operate its
own control from the aspects of its changing geometry.
One needs to ask the proper question about con-
sciousness fact generation. This question was asked by aphilosopher a couple of decades ago, by M. Heidegger
(Heidegger 1959): what brings us to think about this
thing right here right now? The answer, quite elaborate,
to this question will lead to a system architecture choice
that will take us away from reactive or deductive sys-
tems. The system will generate intentionally its con-
sciousness facts, as P. Ricœur understood it (Ricœur
1990). There is no generation of consciousness facts
without intention to think about something. This settles
the question, considered a formidable one, of freedom
to think (Ricœur 1990). One thinks indeed about
everything according to our memory and our intuition
of the moment, but only if it can be expressed as a
thought by the thought producing system.
We develop in this article the main points of a
software architecture allowing a robot to produce facts
of consciousness and we show the results of a prototypeon autonomous robot. The system presented is generic
and can be applied to all computerized process of
continuous data streams, without material body.
Concepts and general architecture
We consider an autonomous robot where the material
body is viewed as a physical substratum, and we con-
sider a software system generating possible represen-
tations of behaviors, that anticipate these behaviors
before doing the actual actions (Brooks 1991). Wewant the robot to be able to develop an adaptive and
purposeful behavior in the environment it is running.
The substratum, the material part, the control—com-
mand part with the physical components indeed, con-
tains the sensors and their memories, representing
information only at the numerical level. The robot’s
behavior is dependent on its actuators capabilities. We
will say that its behavior is adaptive if it is not irratio-
nal, not chaotic, and not strictly determined, but is
necessarily, for itself, an adequate situation in its cur-
rent environment (Mataric 1995). The question is how
to define a system that produces the reasons and the
deep incentives for such a behavior.
Functionality of the system generating artificial
representations
We consider the robot as having the capacity to hold
information in real time from its sensors taking into
account the things of its environment, and to access a
specific internal memory about things and facts. This
general information will be interpreted and continu-
ously generates the current states of a specific system(Fig. 1). Let’s specify the general functionalities this
system must have, with three functional subsystems:
1. The system has a subsystem taking external and
internal information. The external capture can be
achieved using multiple specific neuron networks
for form recognition.
2. It has a subsystem of action that allows the
manipulation of its different motor organs in
reactive or original planned ways.
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3. It must possess a specific subrepresentation system,
with an architecture made of numerous linked
evolving parts, allowing to continuously and
deliberately construct a state of the robot’s situa-
tion in its environment, taking into account the
capacities of its body, and its security.
We will make the systemic hypothesis that these
three subsystems are dependent and co-active: no one
is activated as a strict consequence of the activation of
another, but they reinforce and compensate them-
selves. They are therefore at time competiting and
cooperating between themselves. In addition, we will
state that these three subsystems are always in activity,
defining the running or the continuous robot’s artificial
life. The process (this term is here according to the
common sense of mechanism) that activates and
coordinates the three subsystems is always on. Sub-
systems exist therefore by their conjoined and corre-
sponding activations: the central principle that drives
their global behavior is a linking process that will be
able to analyze itself. We call this process the central
process (Fig. 1). The fact that the system is able to
coordinate its three subsystems expresses its dynamicorganization.
According to the capacities of the two input and
activity subsystems, the robot is immersed in the
environment it will be able to discern as very rich or
relatively poor. It is clear that the system of generation
of representations will be conditioned by the quality of
the production of these two functional subsystems. But
why is the representation system is running in a no
reactive but intentional way?
The central process and the two main questions
The three subsystems: input, action, and generation
of the current representation, are always in co-
activity in a continuous process called the central
process, that coordinates their activation and ex-
presses the system global organizational activity,according to its embodiment. This is a distributed
process. The main questions are why is the system
running and why it stabilized into the current state
expressing something pertinent.
Without elements of answers to these two main
questions, there is nothing constructivist to say about
artificial consciousness.
The notion of generated representation
We are interested a system representing, on its own, itssituation in the environment, to achieve actions there.
That is the so-called ‘‘ representation system ’’.
The representation system
That is strictly a software system allowing produc-
tion, from information picked up at the substratum
level, a purposeful internal representation leading to
a behavioral action or the generation of another
internal representation. This system is based on an
infinite process achieving a strong link between thesubstratum that can feel and act physically on
the environment, and an internal memory allowing
the generation of the pertinent current representa-
tion. This software system will be the computable
transposition of a brain working.
We can propose a constructivist hypothesis about
the representation system architecture.
The basic components
For the representation system, all the used basic
components will be specific proactive processes that
are like oscillators in an organization managing its
control in a distributed way.
This system, according to a real mind, must have
several characteristics and numerous parts, like an
episodic memory, a deep memory, a meso-limbic sub-
Physicalaction
System
InputSystem
System of Representation
Generation of thecurrent situation
CentralProcess
Fig. 1 The three subsystems for the generation of representa-tions in a current situation
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morphological organization. The morphology is ex-
pressed in an abstract organizational space, a geomet-
ric space of shapes (Cardon 1999), that we will describe
later.
Let’s specify what is a state of this system, what is the
configuration of the representation system producing
something of significance. This notion of state takes
into account two notions: a set of local semantic no-tions coming from the basic aspectual entities and a
global notion coming from the morphology of active
entity set:
The system aspectual state
An aspectual state is a construct composed of pro-
active elements that are adapted to for the things
conceivable by the robot, where the proactive basic
element organization is stabilized for one short
moment under the morphological control. It will be
the equivalent of an intelligible idea about some-thing. The state is constructed like a specific dy-
namic set of entities, each of them representing very
local characteristic features, the whole active form-
ing a global structure which is coherent, taking in
account some tendencies, some memorized facts and
some specific configuration.
The representation system and the generation
of meaning
The problem of the generation of a representation withsignificance can be expressed in the following way. The
robot is in an environment where it can distinguish a
lot of things, item by item. Its sensors are active, its
organizational memory is available and it must decide
its behavior here and now. This behavior will be the
result of the current generated representation that will
permit to take a meaningful action, that is:
• To represent something that is going to have a
significance, that is rationally and emotionally
generated in itself, in consistency with its previous
states to respect the continuity,• To feel this representation with artificial emotions,
• To act by its actuators,
• To know that the representation is its own, in the
sense that it is possible to use it later to affect the
next ones, that is to force the next states of
generation.
So, what is the architecture and the capacity of
reorganization of the representation system that would
permit to have these properties, to destabilize its cur-
rent organization from initial tensions, internal and
external tensions, to change its dynamic order of re-
lated parts, to stabilize it in a temporary state that the
morphological system would be able to feel as such,
and to take an immediate decision to act? Could such a
state be seen as the production of meaning by means of
the movements of the objects the system feels, and how
to generate such a state?The solution to these problems requires answering
the following questions:
1. What is the necessary complexity degree to such a
system?
2. What are the architecture and the control charac-
teristics that would permit the temporary stabil-
ization into a state expressing the significance
according to something meaningful in the envi-
ronment or in the memory?
3. How to generate a reason to destabilize the system
that is not an a priori pre-defined reason?4. How to define the system stabilization process,
which must be only temporary?
5. How to define the way the system is able to rep-
resent what it produces as a steady state one in-
stant and that it can use thereafter?
The first hypothesis concerns the very general
characteristic of a system able to generate meaning.
In reference to the typically complex brain structure,
we make the hypothesis that only the systems whose
organization is complex in an organizational way have
the capacity to generate meaning (Cardon 2005). We
will consider a system qualified of organizationallycomplex therefore, that is organized of large sets of
interactive elements and that has a ‘‘ dynamic order
of parts and processes in mutual interactions’’ (Ber-
talanffy 1973; Clergue 1997). Such a system is formed
of a very large number of elements, each of them
having a behavioral autonomy. That will be software
agents. Inter-relations between these elements will
produce the current state as an alteration of some
form. Then, the general system behavior will be
essentially characterized by the reorganization of the
local behavior of these basic elements, by the modi-
fication of their couplings and their internal modifi-cations themselves.
Basic elements: the aspectual agents and the notion
of morphology
The basic entities used in the current representation
construction are dynamic, proactive, rational and ‘‘so-
cial’’. That will be software agents called aspectual
agents (Cardon 1999). The general aspectual agent’s
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structure will be expressed with four parts, more or less
developed according to the agent specificity (Fig. 3):
• A module for knowledge,
• A module for communication,
• A module for behavior,
• A module for action.
We have specified an incremental method of con-struction for a massive multi-agent organization (Car-
don 2004). Every agent will be considered like a simple
enough software entity: it will be a weak agent that
communicates with the others and whose behavior
takes into account the result of these communications.
Aspectual agents represent a lot of categories, de-
rived from ontology, that refer to:
• The space and its different possible description
modes (the permanent and regular shapes)...
• The time and its modes (the notion of time that is
passing out)...• The designation of well-identified things (the
detachment of something from a set of shapes) ...
• The definition of a concept, a relation, a word, a
form, a thing ...
• The situation of an object (the appreciation, the
utility, the worry...)...
• The possibility to manage the organization itself
(the proto-self component elements) ...
All these general and abstract characteristics should
be turned into different groups of aspectual agents that
are active according to some cases and where the group
characteristics are variable. This process will be acti-vated according to the information provided by the
linked sensors aspectual agents.
One kind of aspectual agent is bound to the robot
input and output. While preserving the robotic termi-
nology, these agents are of two types:
• The sensor aspectual agents: they interpret the
information coming from the environment,
• The actuators aspectual agents: they propose an
actual action of the robot’s organs.
All the numerous other aspectual agents are strictly
internal agents. They define concepts and very local
actions and plans: the numeric synthesis of their past
actions, the proposed current action, the possible val-
ues of their future actions. Let’s notice that this notionof plan is strictly local to agent and only defined by the
values of internal variables, allowing memorizing the
details of the local actions.
Agent organization is therefore a system whose
behavior is based on the variable interactions between
the agents (Fig. 4). We will choose at times a finer
granularity in regard to functionalities, and typically
‘‘plurivoc’’ ones, that is to say based on the redundancy
and the plurality of the characteristics. We state that the
system will have its functionalities distributed in no
steady and no consistent agents: for every precise
functionality we will associate groups of agents whosecooperative actions should permit to achieve the func-
tion in question, but also its opposite, inverse, contrary,
near and similar functions... The agentification method
leads to a certain redundancy and large diversity with
regard to faculties of agents. That will be necessary to
permit a complex behavior in an organizational way
and also to make the system operate strictly by emer-
gence. Groups of agents will not be in any way func-
tional but rather versatile, ambiguous and they will be
able to create emerging groups using communications
between them, reifying some specific roles.
A generated aspectual state expresses some signsaccording to the semiotic sense of the term (Peirce
1984), the signs being correlated to characteristics of
real world objects. These basic elements come from
ontology representing the knowledge of the world for
the robot, and are located in its organizational mem-
ory. But the basic dynamic elements associate them-
selves to form the new current emergent state: that is
not a static ontology but a dynamic one. And it is
therefore necessary to proceed to an in-line control of
their activity to force the set of active agents to reach a
coherent and adapted global state. It is not about
forcing the organization so that it reaches a predefinedstate, but to force it locally, in some movements of its
dynamic entities, so that it reaches an admissible state
according to what it contains and also to its geometrical
form. The control will be achieved according to the
morphological characteristics of the aspectual organi-
zation, according to shapes that this organization takes
while activating its entities. It is about defining an
artificial mental map (Fig. 4) by the aspectual organi-
zation movements, controlling its expansion.
Communications
Knowledge
Actions
Behavior
Fig. 3 The general structure of an aspectual agent
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It is common, in artificial intelligence, to select
concepts using some mechanisms with meta-rules. That
is not the case with the system we develop. There are
some available agents, named and qualified with their
semantic roles and coming from the organizational
memory, by the way of ontology of many domains
using the language and the psychology of robot’s
behaviors. Some of these agents tempt to spread out in
a group, and there is the emergence of structured
coherent ones, meaningful of a mental and physical
state of the global system, that is going to make the
current point of attention in the representation. All
these system agents will notbe simultaneously active
and most of them remain fixed, but may be solicited
while activating some others by their accountancies
(Ferber 1995).
Each generated current representation describes it-
self therefore in two ways:
• At the aspectual agents’ level that clearly expresses
by the way of their local semantics expressed in
their states and behavior, the things to which they
are going to make allusion,
• With some shapes that are the geometric confor-mations of the aspectual agents’ activities, in a
specific space we describe further.
The concept of shape is strictly a geometric one: a
shape will be seen like some geometric figures (Fig. 5,
6) as graphs or polyhedrons having some specific
geometrical characteristics (Cardon 2004). Our notion
of representation corresponds to the one of history
for the autobiographic agents of K. Dautenhahn
Fig. 4 Communicationsbetween aspectual agents inthe prototype: the mentalmap at the organizationallevel
Aspectual organization
Morphologic representation
Fig. 5 The morphologyexpressing the form of theaspectual agents organization
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(1997), but where the concept of agent rather means a
robot.
The characteristics of a system generating the current
representations
According to the previous general presentation, we
now focus on the characteristics of the representation
system.
The organizational memory
The generated representation is a construct indeed,
each time formed with an organization of aspectual
agents whose behavior expresses a set tailored to the
current aimed situation, the situation that has been
wanted by organizational tendencies:
• There are interface agents interpreting information
coming from sensors, interpreting them while
transforming the numeric indications in semantic
features,
• There is a current memory of the previous gener-
ations, in order to respect the continuity of the
produced states, the artificial thought produced.
This construction is allowed by the existence of two
fundamental and original structures:
• An organizational memory,• A system of specific in-line control of the repre-
sentation acting during its generation.
The robot’s artificial brain must have so-called
‘‘artificial real life experience’’, i.e., ‘‘ve cu’’ concept
according to P. Ricoeur (1990). It has an event-dri-
ven memory, a memory of events, introduced ele-
ment by element with the numerous links at the
construction stage of the system, but dynamically
enlarged by its actions and behavior. This memory
retraces facts, situations, events, knowledge, cases,
doctrines.... It is based on ontology giving the system
the elements of a specific knowledge and culture.That is not a factual memory but an event-driven
one, under a very particular shape allowing the soft
impairing of each extracted and used element and
putting it in the specific current context adapting it
indeed.
That is a memory delivering some past facts into
the current context for each recall. This memory is
structured to be modifiable by change as consequence
of each present state. Especially, its structure must
permit that each extracted fact is systematically cast
into a form corresponding to the context of the call,
and to inflect also the memorized structure: each callof a fact is a modification of it. A memorized fact is
only a sign that spreads out and recomposes itself in
the new context of generation of the representation,
with some specific shape. This memory will evidently
be based on the aspectual agents organization, but
will be structured into another level. Its original
architecture makes the object of a patent deposit into
USA (US Overt Trademark and Office n2059, 2005).
Fig. 6 The emergence of aspectual groups of aspectualagents linked as graphs on thescreen of the prototype
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The representation system is going to call therefore,
or to undergo by necessity, on the memorized events
and facts of the organizational memory, whose ulti-
mate components will be some proactive aspectual
agents. These awakened agents will tend then, because
that is their nature of agent, to join groups forming the
core of the current representation and inflecting itaccording to their roles. This mechanism we develop is
a kind of work of processes by tendencies (Campagne
2005).
This mechanism of aggregation of aspectual agent
activation operating socially according to their roles
cannot be left without any control. A miraculous
emergence does not exist. There will be in the conduct
of the representation system, in the semi-liberty left to
the aspectual agents, a strong morphological control on
the agent’s aggregations. This control will be carried
out according to semantic agent’s indications with their
roles, using organizational patterns of control (Fig. 7).These patterns control the morphologic development
of the aspectual aggregations, using components co-
activity and specific semantic notions of values, truth,
rights, relevance.... The robot’s artificial brain will be
constructed to generate some artificial classes of con-
cepts, according to its organizational memory but with
limitations introduced at the construction stage. This
specific system of control is also the object of a patent
deposit into USA (2005).
But the robot, in accordance with its body, must
have an equivalent of the mammalian meso-limbic
system: it will feel emotions corresponding to releasesof impulses systematically and solving the embodiment
problem. It will generate artificial thought always in
accordance with emotions.
Fundamental tendencies and aspectual state
The current representation generation is not an
automated reaction to some input. To address the
incentive of the current aspectual state construction, it
is necessary to specify the notion of fundamental
tendency. A fundamental tendency will be the con-
structivist transposition of the impulse concept in the
sense of S. Freud (Freud 1966). Then, we will have to
represent such a concept in the computable way.
Fundamental tendency
Adaptive systems are designed so that they must
satisfy the general needs that presides over their
behaviors and their representations of things in a
decisive manner. These general, multiple and con-
tradictory needs will be called the fundamental
tendencies of the robot. These tendencies reside in
the representation system as major modifier ele-
ments, operating at the level of the morphology of
the representation system.
These tendencies are the reasons driving the
behavior of the robot, first while reorganizing its cur-
rent representation and secondly allowing the action in
an adapted manner in its environment. With the
necessity to act imposed by these fundamental ten-
dencies, the robot will led to solve some various types
of problems and to solve them in an interested manner.
Fundamental tendency, organizational definition
A fundamental tendency is an organizational slantinflecting the construction of the aspectual state,
while giving it some specific characteristics about the
real context appreciation. This slant will operate, via
the morphology, on the set of entities constituting
the aspectual state. It is therefore a promoter ten-
dency of the generated representation.
We consider the fundamental tendencies as numer-
ous and contradictory. In the case where the system
would only have one fundamental tendency each time,
or where all the active tendencies would be strongly in
agreement as a hierarchy with a permanent dominantone, the system would look like a reactive one with an
explicit goal operating with multi criteria choice.
The system must adjust its tendencies while con-
forming the plastic structure producing its aspectual
state with some constraints on the organizational lib-
erty degrees, allowed with its basic elements. The
behavioral consequence of the generation of such a
representation will be itself qualified of adaptive
(Meyer and Wilson 1990).
CurrentRepresentation
Pattern foremotions
Pattern for logicdeduction
Pattern for ask the questionsPattern forrecognition
Pattern formanagement of
memory
Otherspatterns...
Fig. 7 The pattern of control of the representation system
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The fundamental tendency notion will be seen like
a change in the generation of each current aspectual
state. It will be necessary to get away from the
functional specifications for the current state emer-
gence, to feel the notion of leeway and quantifiable
subjectivity.
Architecture of the representation system
We are going to specify the architecture of the system
generating the representations.
The co-active components
This system is composed of specific subsystems
organically bounded by another processing their strong
coupling (Fig. 8). All these subsystems have the multi-
agent architecture.
1. The organizational memory. It is an event-driven,
structured, dynamic memory, representing the ro-
bot’s ‘‘artificial real lived’’. This system must
organize facts, events, phenomena, cases... that it
can use to go to the current generation, which is its
current artificial thought. It will modify its memo-
rized traces while using them and it is clear that the
extracted knowledge will always be presented
according to the context and is never merely
symbolic. Yet, knowledge about facts and events
will be entered in the system under objective
shape, but in an adapted structure allowing dy-namicity,
2. The system of construction of the current state,
composed of the aspectual agent organizations,
expressing the aspects of the different things of the
world represented. These agents are rational and
proactive entities that alter themselves while
functioning. Each of them produces results that are
inputs to others. This system is therefore a network
of processes in re-conformation, endowed with
semantics, which tampers itself while functioning,
according to the sense of Atlan (1995). It builds
features about the perceived current situation
structuring these agents towards some specific
shapes during their activation,3. The system of morphological control of the con-
struction of the current state view as a geometric
aggregation of processes, expressing in a strictly
geometric manner the way the results of compu-
tations and the computations themselves are done
by the agents and what precisely they form in the
whole, in the organizational sense. This system
expresses the conformation produced by compu-
tations and communications: it represents activities
in a dynamic graphs space (Campagne 2005). This
system will be able, according to the conformation,
to really globally act on the actions of the aspectualagents, to control the behavior of the agent’s
organizations,
4. The central process, linking in an organic relation
the system of construction of the aspectual state
with the morphological system, dragging the two
systems in an continuous dialogic action we called
the mirror action (Cardon 2004), where a modifi-
cation in one of the systems immediately drags a
modification in the other one, and so forth, until
reaching a stable state in the co-activity of the two
systems,
5. The system of commitment, leading to the gener-ation of a new aspectual state, able to make active
the aspectual organization and the morphological
system without predefined goal as in the reactive
systems. This system expresses the intentionality
and the liberty therefore to organizationally act. It
expresses the intentionality and essentially oper-
ates at the morphological level.
6. An assessment and spatiotemporal change of the
central process that allows, according to the
rhythm of entities activity, to detect and to achieve
modifications, either brutal as organizational rup-
tures, either slow and periodic, as pulsations, andthat will be the mental shape of the artificial emo-
tions leading to representations and subjective
behaviors.
7. The emotional system, external to the representa-
tion system, operating as the meso-limbic one,
altering the current representation construction in
the representation system with the introduction of
impulses. It strongly uses the morphologic and the
commitment systems, disrupting them.
Organizationalmemory
MorphologicControl fordescription
Anticipationmorphology
Emotionalsystem
Aspectual
organization
CentralProcess
Fig. 8 The representation system and its components linked bythe central process
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Such a system that evolves to represent and to
analyze the conformations of all the active basic enti-
ties can have three types of functioning:
1. The morphological control system only selects the
most active and most applicable entities, according
to metrics it can use (Campagne 2005). Then we
are in the case of a system producing some statesadapted to the current situation, using some
selector operating at the meta level.
2. The morphological control system radically drives
the active basic entities in whole, their global
structure is fixed, but the control allows the basic
entities the maximum of liberty. The system is then
similar to a neural network operating by reflex on
an autonomous entity substratum.
3. The morphological control system is co-active with
the aspectual entity organization, the basic entities
organization continuously evolves. The system of
commitment allows a free-will, because the repre-sentation can develop its action as it wishes. We are
in the very delicate case of the co-activity that is the
foundation of the adaptive systems generating
artificial thoughts. That is the case of our system.
The different organizations of the agent system
In the representation system, basic entities are strictly
rational weak agents: the aspectual agents. These
agents have to compose, by their activities and their
communications, in fact by their aggregations, thecharacteristics of the current state. The choice to use
agents is reasonable, leaving behind the rather reactive
level of the formal neurons architectures.
We set, as a realistic hypothesis, that the consider-
ation of symbolic elementary entities endowed of some
significance at the knowledge level, will permit the
acquisition of a constructible and intelligible current
representation. We state ‘‘entities endowed with their
own tendencies to the significance’’ and not entities
reifying structural concepts and managed with meta-
rules. The existence of such a basic pro-active entityhaving minimal significance characteristics was the
central hypothesis of L.S. Vygotski about the emer-
gence of the thought in the brain (Vygotski 1985). We
follow this hypothesis.
The system will be composed therefore of six agent’s
organizations (Fig. 9):
• The interface agents, bound to sensors and actua-
tors,
• The agents of the representation system:
• The aspectual agents for the construction of current
state,• The morphology agents,
• The analysis agents, analyzing the morphology and
intervening on the aspectual agents about the
construction of the current aspectual state,
• The organizational memory agents.
• The emotional system agents.
The analysis agents are going to provide a cognitive
view of what has been expressed by the geometric and
semantic information coming from the morphology
agents, above the aspectual agent landscape, that is an
interpretation of dynamic graphs indeed.Let’s note that this agent level will have a complex
structure because it will be a co-active set of agent
organizations—aspectual, morphology, analyzed as
InterfaceSystem
Aspectualsensors agents
Aspectualeffectors
agents
Aspectual
Agentssystem
Morphology
Agentssystem
Analysis Agentssystem
System of Representation
Agents of theorganizational
memory
EmotionalAgentsystem
Fig. 9 The five organizationsof agents of the system
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indicated in Fig. 9. In fact, it will not be sufficient to
represent the generation of a representation with
emotional constraint using only these rational agents
and we should use other very sophisticated techniques
of modification of the construction of the emergent
form in the aspectual organization, by the diffusion of
‘‘hecklers’’, i.e., no-deterministic messengers, in the
aspectual agents. The stability of the system will bemore difficult.
Vectorial expression of the agent behavior:
the morphology
To control the aspectual organization, we represent
actions, aspectual agents movements, with some type
of active forms that are essentially the geometric
characteristics representing the dynamic significance of
theirs activations. This interpretation brings us closer
to morphogenetic spaces of R. Thom (1972), with their
characteristics of regular or hard modification. The
specific type of forms we have to use on the dynamic
graphs is the key to the problem of significant emer-
gence, linking geometric forms to semantic and is an
important patent deposit in USA (2005).
The main problem in the manipulation of a large set
of agents is the representation and the control of the
behavior of its organization. We have proposed a
solution for the control that will be the foundation of
our morphological hypothesis. One considers that the
global significance of activities achieved in the aspec-
tual organization can also be determined in a geo-
metric way, by shapes spreading out in a dynamic space
whose measures represent the results of computations,
the speed of computations, the interactions between
computations, the possible collisions of competitors
computations, the modifications of the functions, their
relative importance and their cooperation.
We represent the behavior of a set of agents inde-
pendently of the problems they solve therefore, out of
the specific semantics. We are going to associate to the
notion of behavior of agent’s group the notion of
geometric shape.
Shape
A shape is an element of Rn with a Euclidian metric,
associated to a semantic space as a space of words.
Let’s notice that the aspectual agents being some
rational entities, it is possible to associate with them a
precise notion of organizational state.
Organizational state
An aspectual agent’s organizational state is the
meaningful general characteristics allowing the
description and interpretation of its current situation
at each moment in the time and permitting to pre-
dict its behavior.
It is clear that one should always bring back each of
this meaningful characteristics to an element of R. So,
an agent’s state will be a point of Rm if there are m
characteristics defining the agent’s state. The problem
is to determine these characteristics.
Activity map (Lesage 2000 )
The activity map of an aspectual agent organization
is a representation of the complete set of meaningfulcharacteristics of the agent behavior for a time.
The activity map will correspond of the mental
map of a cerebral activity. To use the notion of
shape, i.e., to represent an activity map by geometric
shapes, it is necessary to first represent each agent by
a vector of state. The characteristics of the agent’s
movement can be defined from the three following
categories:
1. The agent’s appearance, view from the outside, i.e.,
by the other agents. This category characterizes theagent’s situation in relation to its environment,
2. The agent’s internal appearance in front of its goal
to reach. It is about its state in relation to goals that
it must imperatively reach again and in relation
with the roles that it must necessarily assure,
3. The agent’s state at the level of its own working,
i.e., its internal dynamic. That is the measure of the
quality of its same organization and its variation in
time.
Then we are going to define the measure of an
organizational space with six characteristics deducted
from these three categories:
(1) According to the agent’s appearance in its
immediate environment that is according to its own
situation in relation to its acquaintances and its envi-
ronment one keeps the three following notions:
• Supremacy: that is the measure of the fact the agent
is either located in position by force in the relation
to its acquaintances, that it has or not many allies
and if its enemies are or not powerful.
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• Independence: that is the measure of the actions
agent’s autonomy, specifying if it is necessary for it
to find allies to reach its goals.
• Persistence: that is the measure of the agent’s
longevity, its life that can be brief or long.
(2) According to the appearance of the agent’s
internal structure, that is its state in relation to its as-signed goals, one keeps the notions of:
• Easiness: it is the measure of the fact the agent
reached its current state with ease or not. For
example if an automaton expresses the agent’s
behavior, this characteristic measures the ease of
transition, the possibility to go back. This indicator
specifies also the support or the resistance met by
agent’s team to other agent’s team to reach its
goals.
• Speed: it is the speed the agent has to reach its
assigned goals. It is for example the speed of getting
over the states of its behavioral automaton whenthe agent possesses one.
(3) According to the appearance of its internal
organization, that is its working view as a process of
communication between its modules and according to
its structure, one keeps the following notions:
• Intensity of the internal activation flux: it is the
measure of the quantity of information exchanged
between its internal components and allowing to
lead to a visible activation,
• Complexification: it is the measure of its structural
transformation generated by some dysfunctions andhaving required transforming of some elements of
its structure. This measure determines if evolution
is a simplification or a complexification of the
agent’s structure.
• Organizational gap: it is an assessment taking into
account the ability of the agent’s structure to
achieve some actions and lead the agent to have
an appreciation of the distance between its internal
state and the global state that it distinguishes in its
environment. It is the appreciation of the adequacy
of the agent’s situation in its world.
These eight characteristics can be represented
therefore in R8 and permit to associate to every agent
what one calls its vector of aspect as element of R8,
which is an organizational shape (Fig. 10). These as-
pects are represented, while regrouping the agents
according to their activities and communications using
some appropriate metrics, by dynamic graphs where
we study the conformation and the change of confor-
mation (Campagne 2005).
With the clear notion of specific organizational
shapes, we can interpret them matching in semantic
space, finally linking geometry and semantic:
Semantic interpretation of the shapes
According to the specific characteristics of the
organizational shapes we can match the geometric
specificities of the forms in a semantic space, usingthe semantic contained in the agents as roles and
knowledge rules.
The notion of commitment: why the current
representation must emerge?
At this stage, we have an agent representation for
semantic and geometric forms, this agentification
coming from ontology. We have a dynamic system with
a morphological control. Then, it is necessary to specify
what triggers the central process and what makes thisprocess stop one moment, in a specific configuration of
the aspectual organization rather than in another one.
Taking into account these constraints will noticeably
complicate the representation system schema. It will be
necessary to define some structural and organizational
extensions.
The difficult questions about the construction of the
system are the following: what leads the system to start
for a new representation? What is the reason behind
getting the system into action and allowing the stabil-
ization of its conformation one moment? What is this
quasi-stable reached state?It is not about defining a mechanism of automaton
type, that would get the system in action upon recep-
tion of a well-known stimulus and that would produce
a compliant answer in a good manner. It is not even to
define a system that would create action by chance,
while based on a stochastic mode. One must remember
the deep answer to the main question ‘‘why do we
think?’’, given by M. Heidegger in a philosophical
setting a long time ago (Heidegger 1959).
Supremacy
Independence
Persistence
Facilitated
Speed
Intensity of the internal activation flux
Complexification
Organizational gap
Fig. 10 Organizational characteristics of the aspectual agent’sbehavior
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What leads the representation system to hire its
central process will be first an internal necessity at the
morphologic level? The system will not be stable in the
sense that it must constantly activate its central process
to reach a state of consistency. The notion of state will
be a generalized one, unifying calculation and repre-
sentation of this calculation. The state of quasi-stabil-
ity, the stability far from the equilibrium state, is onlytemporary and it immediately leads to the production
of another state, quasi-stable a short moment, and so
forth. The consistency will be insured by the intro-
duction of a rational structure forcing the aspectual
activity, according to its shape and its semantic. There
will be components operating a control at the semantic
level therefore: projection of the morphology of the
aspectual organization on semantic spaces with in-line
analysis. With these characteristics, the system will be
continually active, constantly producing states of
ephemeral stability with different rhythms (awake and
sleep periods for example).We don’t choose to represent the reason to activate
the central process with an ad hoc structure, of which
we would not know what or why it would be activated.
We choose to place the reason of the run into a specific
activity in the morphological representation system
itself. What is going to commit the system to run its
central process towards some current state it does not
know at this instant, is the fact that this commitment is
a latent indication present in the morphology, in the
‘‘presence of the present’’ of the system, using a phe-
nomenological formulation (Heidegger), and therefore
a reason that will be out of the semantics at this time.Very concretely, it means that the ‘‘shutter release’’
activating the central process in a direction of activity
will be a specific anticipatory system situated in the
morphological one. The morphological system de-
scribes the current representation of the aspectual
organization and must also generate the commitment
at a time to operate towards a new representation. The
commitment towards an activity is expressed as a
general form the representation system can have. This
anticipatory system constraints the aspectual organi-
zation, it has ‘‘a temporal step’’ of advance on the
global aspectual activity that the central process isgoing to reduce. Then the central process can run its
systemic loop driving a commitment towards its effi-
cient realization, putting into concordance the aspec-
tual organization, whose form is destabilized by this
commitment coming from the anticipation, with the
morphological system.
The representation system contains therefore, be-
sides the morphological representation, a system
expressing a commitment like a shape that orients the
aspectual organization and therefore modifies its active
organization in some direction given by the tendency of
the commitment. That will be the morphological
anticipation system. The most significant in the repre-
sentation system and in the organizational memory, the
most important in the inputs, the most important
among its latent geometric traces, the more in tension in
the organization, bring on the representation of whatwould be actually computed by the aspectual organi-
zation, in the dialogic loop of the central process. And
to be coherent with a non-reactive architecture, the
anticipation system will be complex, containing always
several commitments that will be possible to serve as
initiators for the central process. These commitments
will be produced by the fundamental tendencies evoked
before, according to the organizational memory.
The architecture of the morphological system is
therefore the following:
1. A morphological system of description of theaspectual representation, that geometrically ex-
presses the current state of the aspectual organi-
zation,
2. A morphological anticipatory system, generating
some shapes allowing the anticipation of the
aspectual organization with specific geometrical
conformations.
These two morphologic systems, that are multi-
agent systems, oppose themselves, alter and coordinate
themselves until they constitute only one coherent and
compliant geometrical representation of the aspectual
organization. The anticipatory system, as its nameindicates, hires the central process into a direction,
according to ‘‘certain aim’’ P. Ricoeur (Ricoeur 1990)
would say and this process, in its organizational back-
ward and forward motion between aspectual and
morphological systems, reinforces the commitment,
tampers it and, maybe, transforms it.
Now that we know that the representation system
ineluctably must destabilize itself to function according
to internal latent tendencies, what makes it stabilized?
The answer will be clear. When there is conformity
between the destabilized morphological space by its
commitment and the aspectual organization, whenthese two systems are coherent at the point of view of
the central process, when the central process leads to
an organizational stationary point, the central process
stabilizes organizations one instant.
But this state of relative stability must be memorized
into the organizational memory. This memory is a
complexsystem in the way that it is a system ‘‘sensitive to
its initial conditions’’ and the introduction of a new ob-
ject is itself a destabilization. This insertion immediately
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leads to the explicit construction of a new current state,
since the reduction of this destabilization is the running
of the system’s main principle. The fact that the system
generates a quasi-stable state that will be a new internal
object in the active organizational memory, ineluctably
leads to the destabilization of the representation system
by the fact that its central process is always in activity. No
state of equilibrium exists, the fundamental tendenciesare multiple and are immediately active on each ob-
servable state. The anticipatory morphology leads the
representation system in a new reorganization, accord-
ing to the insertion of a new object into its memory, and
the system continues its successive organizational
transformations. Generation of artificial thoughts is a
process like a fall without end.
So, we can set a precise definition of a system gen-
erating artificial thoughts:
Definition of system generating artificial thoughts
• Such a system is a software system binding an
unceasing informational stream produced by a body
or by many bodies.
• The system is composed of a lot of computable
entities, each of them having a cognitive foundation
upon a semantic level, having the property of
proactivity, of autonomy and structural evolution
and each entity can be systematically recombined
with some others.
• These entities form a very structured organizationlike a set of artificial real life experiences, i.e., a
‘‘ve cu’’, that is an organizational memory com-
posed of strongly linked forms that can continu-
ously increase themselves by re-combination,
• The system has a distributed control level on its
organizational memory, expressing multiple funda-
mental tendencies that are impulse controls, at the
rational and emotional meaning. These controls
have the form of morphological constraints on the
aggregations of the basic entities.
• The system, at the level of the morphology of its
entities, can construct, of its own and according toits current global state and tendencies, emergences
of internal forms.
• The generated emergences are geometrical objects
composed of many proactive structured entities,
linked to the objects of the real world, in the way of
a relation ‘‘type of form–type of significance’’. An
emerging form always produces the activation of
other objects in relation with this one, allowing the
continuity of the artificial thought generation.
• The system has always some relations between each
of the emerging object it generates and the other
semantically or emotionally close objects of its
organizational memory. All the generated and
manipulated objects are linked ones.
• The system can observe, manipulate, and play
indeed with the internal objects it has produced at
each time and, by this on-line process on its owninner objects, it can have the sensation of thinking
itself about these objects, in a morphologic and very
dynamic space.
• The system has a complex dynamic organization
having, in its process of generation of emergent
forms, the complexity of the number of parts of a
set.
The artificial emotions
We are going to define the architecture of a specificagent organization managing the production of emo-
tions like pleasure or pain. The fundamental tenden-
cies, to be able to finely modify the current
representation, will be expressed as the characteristics
of some organizations of specific agents on the repre-
sentation system, modifying the geometrical shape of
this organization.
Self-adaptive component
That is a software component whose actions are inthe way of its own satisfaction and where the results
of these actions modify its internal organization: the
component evolves under its own tendencies.
Artificial emotion
An artificial emotion will be essentially seen as the
rhythm of some self-adaptive components func-
tioning in an emerging way in the aspectual agent
organization, functioning by synchronized loops
whereas the system undertakes some external typi-cal action. In this way, the system and the robot
adopt a specific behavior.
Emotion and dynamic of the agent’s groups
The fundamental tendencies will be global character-
istics leading to the re-organization of the aspectual
agent’s organization. It is a strong organizational
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hypothesis, since we will represent tendencies by types
of shapes on the movements of the agent’s organiza-
tions.
The geometrical hypothesis about tendencies
The geometrical hypothesis, with regard to thefundamental tendencies expression, states that the
tendencies can be represented by some kinds of
deformations of geometric shapes in the dynamic
space expressing the movements of aspectual agents’
organization.
In fact, we have to deform the aspectual organiza-
tion indeed, giving a specific rhythm to this re-confor-
mation. According to this point of view, an emotion is a
geometric alteration of the aspectual activity pertain-
ing to specific behavior of the robot.
The architecture of a system generating emotions isradically different of an input–output one, running
through some predefined steps. We have to define the
specific notion of ‘‘control of its own’’, i.e., a control
managed by the continuous feedback of the action of
control itself. We use such a control for a system pro-
ducing fuzzy states like the emotional ones, composed
of proactive agents, generating internal cycles of
activities with specific rhythms, according to the actu-
ally observed process of emotions in the brain and
corresponding to different types of actual actions or
behaviors. This architecture will be founded on the
aggregation and the breaking of aspectual agent’sgroups.
Expression of an agent’s group
Because they are proactive, organizations of aspec-
tual agents can be represented in an organizational
way, where the form of the activities and the links
between agents directly lead to an actual activity of
the system, in a continuously adaptive action and
reaction with the environment.
Basic computable component producing
the emotion: the computable oscillator
The biologic presentation of the emotional activity
describes the existence of neuron domains speeding up
in some loops, working each other’s for the propaga-
tion of flux of activation. We must build a system
where the form of activity is made of emergent
feedback loops, positive, negative and additive loops.
We precise the basic architectural elements of the
system with the following component: the computable
oscillator , a computable interpretation of the well-
known Wilson–Cowan oscillator (Wilson and Cowan
1972).
Computable oscillator
A computable oscillator is an organization of
aspectual agents whose activation quickly forms and
by its own functioning, many cycles of activity with
specific intensity and speed. It has a shutter release
on the form of specific emotional agents.
Such an oscillator is an organization of weak soft-
ware agents coordinating them, modifying their links,
synchronizing with some others and acting on the
aspectual organization. The oscillator leads to the
emergence of an aspectual agent structure, distinct of
the other aspectual agents of the organization. Such a
group must emerge to control itself and the other
groups. The organization passes from a state to another
where a looped process transforms a group of agents
into an oscillator. Mathematically, this group is an
emergent sub-graph in the coupled activation graph of
the agent’s organization. Such an emergent oscillator
leads to a specific adaptive activity of the robot and
must control other attempts of emergent loops. Its
shutter release is composed of specific emotional
agents.
The aspectual agent organization will be formed of a
structured set of such computable oscillators, allowing
a global and local backing and the process inhibition or
stop. There is no central controller in this system that is
distributed among the emerging components and in
their synchronization using negotiations. The system
will function, after the action of the shutter releases, by
a control of its own and a regulation of its own also, of
its oscillators with local limits cycles and with a more or
less conservative faculty.
The system state allowing artificial emotion
To raise an emotional process, i.e., a particular activity
of the agent’s organization with a typical correspond-
ing behavior of the body, it is necessary to start from a
state that is clearly a neutral one. We will call ‘‘ low
state’’ the state without any emotion or achieving an
automatic behavior. In this state, only some specific
aspectual agents are operational.
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Low state
It is the state where the aspectual organization is in
an automatic activity or achieves a preprogrammed
automatic reactive action.
In the low state, the inputs will not trigger anythingemotional and will amount only to reflex actions. We
have to give the autonomy to the groups of aspectual
agents, in the sense of a general functioning with
internal loops. The system must adjust to consolidate
for a while its structure: some groups of agents are in
progress and will supplant some others according to a
rhythm, launching some external actions according to a
specific fashion. Others agent’s groups can work in the
background, without orders sent to the interface
aspectual agents, but they would be later in an emer-
gent state (phenomenon of domination). The adequacy
between the external flux of information and thecomplex activation of aspectual agent’s organization
will allow the emergence of some self-adaptive com-
ponents.
The development of the emotion: from the signal to
the incentive
We set the hypothesis that each sensor that is not a
strict alarm sensor corresponds, in a way, to an artificial
sense in the system. So we have:
• Sensors or routines of vision, temperature, touch ormotion tracking...
The system feels its environment by different means
that we call it ‘‘artificial senses’’. For each sense will be
associated, whatever it is, to a state of activity:
• Active or not active sense, with notion of intensity
if the sense is active.
• Active sense in an admissible way (towards the
pleasure) or a no regular way (towards the pain).
These sensory characteristics can be easily repre-
sented into the aspectual agents bound to some actu-
ators agent’s, the interface agents.From some signal considered like a starting shape
putting in action the inputs of the system, there is
generation of an incentive, which is a general tendency
towards action. This incentive will be the shutter re-
leases on the aspectual organization. It makes this
aspectual organization focus towards some goal, with a
plan of action defined into the analysis agents, a global
plan but with local indicators of sub-plans on different
aspectual agents. The system has to maintain this plan
during a time without tampering it too much and
especially while valuing it. The incentive is therefore a
meaningful modifying activation of the current low
state with direct effect into the different organizations
of agents leading the representation system towards a
typical behavioral activity aiming at some goal. For
example, this activity could be for the robot ‘‘to take a
pen for to make a fuss of it’’, ‘‘to drag quickly on theright of the window’’ for its satisfaction and pleasure.
There is a commitment to an actual physical activity
corresponding to the modification of the aspectual
organization. This signal is going to release:
• According to the state of the system,
• According to the environment characteristics,
• According to the complexity of the emotional
system,
• In an irrepressible manner, and not randomly.
The signal would be generated by a specific orga-
nization of agents having no direct contact with theaspectual ones, but while taking into account their
morphologies. In each case, the signal will be trans-
mitted to aspectual and analysis agents to lead the
system in its entirety towards a goal. We will represent
the set up of this signal while using the fundamental
organic tendency paradigm, like an emergence in a
specific agent organization, taking only its information
from the morphology agents (Fig. 11). Then the origin
of the signal is considered as virtual.
Once this signal is activated, the representation
system has to generate a global behavior. The signal is
sent therefore to the agent organization of analysis,activating a pattern of requisite typical behavior. This
pattern of behavior, in fact a plan (flight, approach,
seizure, restraint, gesture of a member...) is managed
by the analysis agents, in a context according to the
system’s current possibilities, and taking information
from the aspectual agents. That pattern leads to the
Fig. 11 The agent architecture of the system generating theemotions
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generation of a new specific behavior taking into ac-
count the represented situation. This plan is initially
not very precise but it becomes clearer in its progres-
sion. It explains:
1. The contexts of the past activities of the robot and
the histories referring to that current case,
2. The immediate undertaken action,
3. The going forward possibilities if the plan either
succeeds or fails.
This global plan of action is transmitted to aspectual
agents and then generates a lot of specific plans (with
their local parts) for the aspectual actuators agents.
This generation is negotiated quickly between them
and corresponds to a behavioral scheduling. There are
therefore behavioral aims defined in a global manner,
with indications driving the local injunctions to aspec-
tual agents linked to actuators. One will say that there
is setting up of an incentive: an objective splitting into
operational sub-objectives, into a specific aspectual
agent’s activity. This incentive will be expressed in the
incentive agent organization and will lead to a specific
form in the system morphology (Fig. 12).
The artificial incentive
The artificial incentive is a global tendency ex-
pressed by a specific agent organization, the incen-
tive agents, activated from the observation of some
characteristics in the morphology system. This ten-
dency leads to a constraint on the aspectual orga-
nization, leads to a general plan of action distributed
into different groups of aspectual agents. This plan
brings about some specific plans with strong coeffi-
cient of intensity into all the aspectual agents. The
incentive is expressed as a morphological form,
which is the ‘‘ form on want to reach here and now’’.
Agents that generate this incentive, that causes it, are
the incentive agents, strongly linked to morphology and
aspectual agents of the representation system. These
agents speed up from a particular recognition sign inthe current morphological organization. In return, they
force some self-adaptive components to manage the
system towards some kind of functioning while first
soliciting analysis agents. They create a wanted form,
i.e., the defined current goal, in the morphology that the
aspectual organization will have to reach.
This organization of incentive agents is always active
in the representation system, but with more or less
intensity. It constantly observes the state of the mor-
phology of the aspectual agent organization and gives
out, at the good time, a specific signal launching the
process ‘‘incentive–emotion’’.
General algorithm Begin
• Continuous activation of the incentive agents
• Morphological survey of the aspectual agents
• Emergence of an incentive signal into the organi-
zation of incentive agents and of a specific form
(the wanted form) into the aspectual morphology
• Activation of the analysis organization from this
signal and from the wanted form and generation of
a behavioral pattern into the analysis agents
• Development of the incentive
• Generation of a typical behavior (form, goals and
sub-goals)
• Injunction of activation to aspectual agents
• Corresponding activation of the aspectual agents
End
We have to define in what manner the system is
continuously maintaining the incentive during that
time, notably defining a ‘‘center of the artificial plea-
sure’’ (Fig. 12). That will be the realization of the
emotions.
From incentive to satisfaction: the artificial pleasurecenter example
We focus on the emotion of pleasure, as an example of
emotion. From a signal produced by the incentive
agent’s organization, the system generates an incentive
altering the analysis agent organization. That leads the
system towards some typical behavior. We have to de-
fine a control system allowing a very flexible behavior
allowing its adaptation holding a general organizational
Aspectual agent
Organizations
Agent organization of
morphology
Agent analysis
organizationPhysical expressed
situation
Agent organizationof
Satisfaction
Incentive
Fig. 12 The general architecture of the system generating theemotions
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antagonistic action of impulses towards quiescent
states (Freud 1966).
Algorithm of organizational emergence
or the representation system
The general operation of the system is the competition
between numerous processes aggregated into groups,
expressing some specific actions and leading to the
emergence of some specific aspectual agent’s groups,
i.e., the artificial current thought. The following
algorithm (Fig. 14) describes the generation of a new
state in the representation system, using competition of
processes and using the concept of emotion as a
modification of the current emergence.The production of the representation system man-
aged by these competitor processes is the global state
of the aspectual agent’s organization, where the mor-
phological system and the aspectual organization are in
conformity, under an expressed commitment that al-
lows putting them into conformity. Such a state, steady
for a short time, will be called an organizational
emergence. That is precisely the temporary fixed point
InputsPhenomena
expressed by
aspectual
agentss
General morphologie
Current
morphologyMorphology
of incentive
Fig. 13 The form of therepresentation system and theinterpretation as an emotionof pleasure.
General Algorithm
[To activate the emotional system][To activate the central process]
General infinite loop
Begin
[ If the current emotion is adapted to the situation]
to preserve this emotion as much as possible
to distort the aspectual organization activity according to this emotion
[ If a latent tension exists in the anticipatory system]
to destabilize the representation system from this commitment,
to distribute the structural modification across the entire representation system
to modify the current emotion
[ If the system of morphology is active]
to activate and to modify the aspectual organization using the central process organically linked to the
morphological system
[ If the aspectual organization is active]
to activate the description morphology system using the central process
[ If the central process is again in action of synchronization]
to activate the aspectual organization and the morphologic system in a co-active way
to reinforce the anticipation residing in the anticipatory morphology system
to modify the emotional state as much as possible
[ If the central process doesn't distort the aspectual organization anymore]
to express the emergent current state that is the best for the current artificial thought
to memorize the current state as an internal mental object that the system can use later
to destabilize the morphology system while allowing the development of a new commitment from the current
state
to look for a new current emotion
End
Fig. 14 Algorithm of
generation of meaning on therepresentation system
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of the central process, the emergence of the meaning in
the representation system, the concept of meaning
being understood and interpreted in a strictly organi-
zational way.
Emergence of the meaning
In the representation system, based on a central
process coupling aspectual organization and mor-
phological system, the emergence of the meaning is
the organizational fixed point of the central process
leading towards a specific conformation. This state is
memorized, as an internal object the system is able
to manage further, insuring its continuous learning.
It is obvious that the structure and the characteris-
tics of the aspectual and morphologic systems are more
complex in the actual implementation than the
description we have presented. Particularly, the exis-tence of latencies generating commitments into the
representation system, is not so simple and brought us
to introduce the concept of local attractors (Thom
1972), seen as fields that create the emergence of local
agent’s aggregations in the different agent’s systems.
The morphological system is not concretely repre-
sented as totally distinct of the aspectual one: the two
systems, aspectual and morphological. They are inti-
mately mingled in one vast agent’s organization
allowing the evolution, the creation of new morpho-
logic shapes and of next aspectual agent, without
external intervention. The system will be composed of different types of evolving agents. The model pre-
sented distinguishes between them, to understand and
to study them. The aspectual organization is an orga-
nization expressed by its morphology, and the mor-
phology is another organization, rather virtual,
allowing the aspectual one to have a specific behavior
according to its activity. It is therefore, for the repre-
sentation system, a strictly self-adaptive system, a
sensitive observer of its own activity, as J. Pitrat spec-
ifies about the consciousness property (Pitrat 1993).
Such a system allows the representation of the
artificial self-awareness notion. Its activity, very adap-tive to its own conformations, appreciable to initial
conditions constantly changing and coming from
internal changing manageable objects, speeding up by
commitment caused by the structure of its morpho-
logical system, permits to observe itself in its action of
computation, in the generation of some explicit
aspectual emergences from the morphology of the
representation space. These explicit emergence states
will be able to be distinguished later as internal objects,
and so, while following its tendencies, the system will
be able to know itself, as the author of its reorganiza-
tions while causing them at will. The mechanism
allowing this self-observation, if we insert it for each
emergence, gives to the system some aptitude allowing
to make a strict distinction between itself and the other
things of the environment it interact and is co-active,
i.e., to be conscious of itself (Ricoeur 1990). The notionof perceived own embodiment then will become
clearer.
Results
The model and the developed concepts were applied
today to the behavior of an AibotTM robot (Camus and
Cardon 2005). The generation of representations pro-
totype, which is the first implementation of the model,
allows:
• To capture information from the robot sensors,
• To constitute an organizational memory and to
enrich it while using ontology, using Man–Machine
Interface,
• To define a set of parameters for the system
generating emotions,
• To generate on-line current representations as
organizational emergences, according to an organi-
zational memory, with an always active emotional
state and the perception of the things of the
environment it can intentionally manipulate,
• To visualize this current state on a Man–Machine-Interface.
The system is experimented on Aibo ERS7 robot,
using a wifi connection between the system loaded
on a classical computer for a good performance and
the robot’s body. The robot can evolve cleverly in an
unknown environment, in which it must be able to
collect data, analyze, make-decisions, act according
to its self-defined goals. The approach of the deci-
sion-making allows to divide these capacities in six
crucial levels:
1. Represent a contextual situation.2. Direct the attention on particular elements (ob-
jects or actions on the environment).
3. Feel emotions about these selected elements.
4. Build behavior action plans.
5. React on the focused object, on a systemic loop
(adaptation of the current plans).
6. Memorize the action when achieved.
These six levels are developed under the multi-
agent paradigm. The system is developed with the
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system Oz/Mozart. Oz is a multi-paradigm language:
scripting programming, object programming, logic
programming, constraints programming. It allows us
to use different paradigms such as the concurrency,
to develop a multiagent system with an asynchronous
communication using the message passing, or the
constraint programming to create different action
plans.Some results are accessible on the web site:
www.artificial-brain-project.com. We are in the process
to set up a new venture for the actual and complete
implementation of the system, including now the Freud
psychoanalytic theory.
Conclusion
It is really necessary for the construction of an artificial
consciousness, to have a specific theory of the mind,
and a really constructivist one. May be I shall writeabout my theory of the mind one day...
The emergence of meaning in the behavior of an
autonomous robot was presented like an ephemeral
stabilization of a complex system in an organizational
way, a system able to observe and feel itself using an
artificial brain. The meaning emerges like a re-con-
formation of multi-agent systems expressing them in an
imperative manner, as a geometrical observation of
large groups of agents. The fact that the behavior of a
robot is capable of significance is founded on a process
of strong link between computation of multi-agent
organizations and semantic interpretation of the mor-phologies of their computable activities.
The importance of such a linking process, binding
the parts to the whole, binding activities of groups of
agents to their significance expressed by morphology, is
really important. It is the fundamental principle of
activity of the self-adaptive systems. It generalizes the
notion of feedback and systemic loop (Le Moigne
1990) and opens onto the autonomous system with the
notion of generation of expressive states as construc-
tivist emergence.
A system that generates a meaningful behavior
while using the capacities of its body, while proceedingan organizational emergence in massive multi-agent
systems, has a complex structure, conceptually and at
the level of the implementation, that satisfies the re-
marks of J. Searle on the notion of infinite background
(Searle 1992). But this organization can also produce a
representation of itself, of its own morphology ex-
pressed as an internal managing complex object, while
opening thus on the notion of ‘‘its own internal’’ ob-
jects. Then the system can use its morphology as
commitment to act, and morphology is at the same
time geometric and cognitive, it is the sign of an
organizational semiotic, it summarizes the process of
reorganization with its result. And the result of this
constructive self-observation can be delivered by the
system to each human observer. In this way, such a
system can express itself as the author of its internal
activities with its own intentions, rather than a gentlesystem displaying values merely to its human users.
Then the difference between such a system expressing
activities by itself according to its own intentions and
another one that would proceed to displays predefined
information in a well rational adapted manner is con-
siderable and makes a rupture in the field, very vast
today’s, of the computer science.
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