• Artificial Brain Project 2003-2008

    System Generating Consciousness Facts


    Authors: Alain Cardon, Mickaël Camus and Jean-Charles Campagne

  • About

    The aim of our researches is the fine transposition of the brain activity in the computable science, and not only a partial simulation of the reasoning domain. The final product is the construction of a general system producing artificial mental representations: that is a software system producing artificial consciousness facts.

    So, this system will have five components:

    • An organizational memory, that is a large memory of facts, knowledge, rules and events where anything coming from this memory is systematically adapted to the current context. Such a memory is not a knowledge base but is a continuous dynamic interpretation of knowledge.

    • A subsystem building in a strictly constructivist way the current artificial idea here and now: that is the construction of the current idea like the well-controlled activity of some large agent organization.

    • A subsystem generating emotions as the alteration of the activity of the subsystem expressing the current idea, that is some specific field altering the focus of this generation, according to the specificity of the emotion.

    • An input–output subsystem linking the system producing artificial consciousness facts with the body of a robot or any software data flow.

    • An interface subsystem expressing the mental map, that is the representation of the current artificial consciousness fact with the reasons of this generation.

  • The Project

    We are interested in the notion of consciousness fact, which is, for us, the fact that an individual endowed with a brain can think of something related to his position in the world right here right now. It is not about the continuity, or the performance, nor the profoundness of the thought, but it is about thinking of something in a knowable manner and which can be specified from a linguistic or mathematical angle, without it being an automatic and predefined response to a given situation.

    By analogy to the notion lengthily investigated by philosophers, psychologists, neurobiologists, we will pose the question of artificial consciousness: how can one transpose the fact of "thinking of 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. The system will have intentions, emotions and ideas about things and events related to it-self. The system would have to have a body that it could direct and which would constrain the system. It would also have to have a history, and intentions to act and, most of all, to think. It would have to have knowledge, notably language knowledge. It would have to have emotions, intentions and finally a certain consciousness about itself.

    We will name this system, by sheer semantic analogy, an artificial brain. However we will see that its architecture is quite different from living brains. The concern is transposing the effects, the movements; certainly not reproducing the components like neurons and glial cells.

    We will keep in mind principally one characteristic of the process of thinking unfolding in a brain: there is a complex neural, biochemical, electrical activation movement happening. This movement is coupled to a similar but of a different mode in the nervous system deployed in the whole body. This complex movement generates, by selective emergence and by 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 following thought which can be similar or different. This is the very complex phenomenon that we have 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 reconfiguring recurrent and temporized flow. 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 of the artificial brain. For one, flows are complex images, almost continuous; for the other, these are dynamical graphs which deformations are evaluated topologically.
    • 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 consciousness fact generation. A philosopher, a couple of decades ago, M. Heidegger, asked the proper question: what brings us to think about this thing right here right now? The answer, quite elaborate, to this question will conduct to a system architecture choice that will take us away from reactive or deductive systems. The system will generate intentionally its consciousness facts, intention as P. Ricœur understood it.

    There are no consciousness facts without intention to think. This settles the question, considered as a formidable, of freedom to think. One thinks of everything according to his memory and his intuition on the moment, but only if it is expressible as a thought by the system producing thoughts. Some might see something infinite in this process, however it is not our case. A finite set of component which movements occur in a finite space has only a finite number of states in which it can be. Also, as the permanence of the physical real apprehensible by the sense is very strong, the preoccupation to think by man is quite limited, in his civilizations. Let us point out that artificial systems that will think artificially will be able to communicate directly at the level of forms of the ideas, without using a language mediator, and hence, would be co-active as well as being numerous in space.

    For different reasons, numerous people think that the path of artificial consciouness investigation should not be taken at all. We are taking this path, without looking back, until the actual development of the system and its validation.

  • Results

    Here, new results with the last version of the prototype which is an Auto-Adaptive System (AAS) to control robots with mental states, learning and emotional alterations. It is this platform which will allow us to move towards the Artificial Consciousness, the fundamental aim of this project.


    • Sensors and effectors dynamic creation.
    • Ontology distribution in a multi-agent system.
    • Memory pattern (acquaintance between agent).
    • Data processing in real time.
    • Role modification in real time.
    • Acquaintance modification in real time.
    • Agent emergence with sensors value in input.
    • Morphology analyze.
    • Focal point at a time t.
    • System control with goals.
    • Sensors modification dynamically.
    • Management of several brain simultaneously.
    • Mental State alteration with emotions.
    • Neural network API: simple forms recognition.
    • Internal morphology on acquaintances in each role. 

    In progress:

    • Goals generation.
    • Mirror process: representation of the representation. 
    • Incubator system to increase learning (a brain is valid for a multitude of robots).
    • Dynamic learning with an ontology update. 
    • Agent distribution on a LAN.


    • Ontology with system goals, knowledge and experiences:
    • Real time treatment of sensors values. 
    • Mental Card, the knowledge global activity in real-time:
    • Geometrical form creation with a deformation of the ontology plan:
    • A morphology analyse and the creation of the focal point, the current "Thought" of the robot:
    • A robot natural behavior:

    So, we have this system: A Systemic Loop in an unstable Environment.

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  • Experiments

    We present a list of experiments which used the last prototype of the Auto-Adaptive System (AAS). Each experiment has a specific domain such as robotic, game or space (and so on), a title and a goal.Experiments are based on the behavior of the system. We observe the emergent knowledge (simple knowledge or variables with specific type and value) according to the goals. This observation allows us to know if the system is directed towards the programmed goals. To observe the system behavior, we have developed a graphical user interface describing the different links between variables of the system. We use a test platform with Aibo ERS7 by Sony for the robotic domain to process different specifics scenario.

  • Technologies

    Currently, we have two software. The first (OTC) is used to execute the different experimentations. The second (MorphoViewer) is in test, it is a system of visualization.


    Ontological Transcription Core (OTC)


    OTC (Ontological Transcription Core) is a software based on a massive multi-agent system connected in real-time to a hardware or a software organism to manage entities. This system is an integration layer allowing justified decisional autonomy.


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    • Domain: we address one of several domains of the real world. We have to manage, to supervise or to control.
    • Knowledge: the domains we take into account have ontology expressing their characters.
    • Transcription: the ontology is automatically loaded with their structures into the OTC without programming, generating a lot of agent's organization.
    • Aims: we define the objectives of the problem in some situation, the actions we have to execute in some context. We can balance these objectives, and we could modify them on-line, during the running of the system. 
    • Running: the system is an infinite systemic loop between: 
      • the flow of input, 
      • the generation of a representation of the current situation with proposition of decisions about this input, taking into account the past events, loaded into the system memory, 
      • the generation of a sophisticated interactive viewer, 
      • and the actions of the users.
    • Viewer:  the viewer is a virtual world allowing to load the knowledge into the system to act on the parameters and to express the interactive current representation.



    MorphoViewer is an abstracted virtual world allowing to load the knowledge into the system to act on the parameters and to express the interactive current representation.

    Focal point's definition: the focal point is, in the system, the current emergence of the significant information and forms.

    The aim of the MorphoViewer system is the production and exhibition of interactive emergences.



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    A monitoring with two updating interface:

    • an ontology update, 
    • and a focal point update.

    Focal point is composed of spheres. Spheres are specific conceptual kernel with a symmetric relation between them. The whole of the spheres follow a defined order forming the order of emergent representation in constant modification according to inputs.




    One Focal Point​

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    Several focal points: decision-making group

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  • Publications

    Between 2000 and 2008.


    • Knowledge Extraction from Web Pages with an Auto-Adaptive System, C. Havas, O. Larue and M. Camus. Invited speaker, 12th CSCC, Heraklion, Crete, Greece, July 22-25,2008.

    • Morphology Programming with an Auto-Adaptive System, M. Camus. The 2008 International Conference on Artificial Intelligence, ICAI'08, WorldComp'08, Las Vegas, Nevada.

    • Artificial consciousness, the hard problem, A. Cardon, Invited speaker, ISC 2008, Lyon June 9-11.

    • Knowledge Organization Using Shapes Extraction from Web Pages in an Auto-Adaptive System, C. Havas, O. Larue and M. Camus. WSEAS Transaction on Systems, Issue 5, Volume 7, May 2008.

    • Emotions Generation and Knowledge Organization in an Auto-Adaptative System using Shape and Color Recognition, C. Havas, O. Larue and M. Camus. Applied Computer & Applied Computational Science 2008 (ACACOS'08), China. Invited paper, WSEAS Transactions and NAUN journal, a springer verlag volume.

    • Knowledge organization with pattern recognition in an auto-adaptative system, C. Havas, O. Larue and M. Camus. Artificial Intelligence, Knowledge Engineering and Data Bases 2008 (AIKED'08). University of Cambridge (UK). Extended version in WSEAS Transactions.


    • La complexité et le paradigme morphologique dans les systèmes multi-agents, A. Cardon, pp. 601 - 616, Revue d'Intelligence Artificielle, Volume 21/ 5 – 6, 2007, éd. Hermès.

    • Generic auto-adaptive system to control robots or sofwares entities, M. Camus, PhD thesis, University of Paris VI, France. 

    • Rencontre de modèles cliniques et robotiques : du saut entre les organisations psychiques, P. Marchais, A. Cardon, Annales Médico-Psychologiques, p. 122 - 129, Volume 165, n° 2, 2007.

    • Towards emotional decision-making, M. Camus, A. Cardon. Innovative Concepts for Autonomic and Agent-Based Systems, LNAI 3825, Springer, 2007.

    • A self-adapting system generating intentional behavior and emotions, A. Cardon, J.C. Campagne, M. Camus. Innovative Concepts for Autonomic and Agent-Based Systems, LNAI 3825, Springer, 2007.


    • Artificial consciousness, artificial emotions, and autonomous robots, Cognitive Processing, A. Cardon, October 2006, Springer Berlin / Heidelberg.

    • Mickael Camus, Alain Cardon, An adaptive system to control robots: ontology distribution and treatment. The 6th WSEAS International Conference on Simulation, Modeling and Optimization. Lisbon, Portugal, September 22-24, 2006.

    • Mickael Camus, Alain Cardon, Dynamic programming for robot control in real-time: towards a morphology programming. The 2006 International Conference on Artificial Intelligence. Monte Carlo Resort, Las Vegas, Nevada, USA.


    • Jean-Charles Campagne, Systèmes multi-agents et morphologie, Thèse de doctorat en informatique de l'Université de Paris 6 (Université Pierre et Marie Curie), septembre 2005.

    • Mickael Camus, Alain Cardon, Towards emotional decision-making , Second GSFC/IEEE WRAC 2005 : Workshop on Radical Agent Concept, NASA Goddard Space Flight Center, 2005.

    • Alain Cardon, Jean-Charles Campagne, Mickael Camus, A self-adapting system generating intentional behavior and emotions, Second GSFC/IEEE WRAC 2005 : Workshop on Radical Agent Concept, NASA Goddard Space Flight Center, 2005.

    • Alain Cardon, La complexité organisée, Systèmes adaptatifs et champ organisationnel, éd. Hermès-Lavoisier, Paris, janvier 2005.

    • Alain Cardon, Jean-Charles Campagne, A self-adapting system generating emotion, SID 2005: Social intelligence design workshop (SID 2005), 2005.


    • Alain Cardon, Modéliser et concevoir une machine pensante, Approche de la conscience artificielle, éd. Vuibert Paris, mai 2004.

    • Jean-Charles Campagne, Alain Cardon, Toyoaki Nishida, Étienne Collomb, Using morphology to control a multi-agent system, an example, 2nd European Starting AI Researcher Symposium (STAIRS’04), 2004.

    • Jean-Charles Campagne, Alain Cardon, Étienne Collomb, Analyse et pilotage de systèmes multi-agents massifs, JFSMA 2004, Journées Francophones sur les Systèmes Mutli-Agents, 2004

    • Jean-Charles Campagne, Alain Cardon, Toyoaki Nishida, Etienne Collomb, Using morphology to control multi-agent systems, Third NASA-Goddard / IEEE workshop on formal approaches to agent-based systems "FAABS III", 2004.

    • Jean-Charles Campagne, Alain Cardon, Toyoaki Nishida, Étienne Collomb, Using morphology to analyse et steer large multi-agent systems at runtime, Selmas 2004.


    • Jean-Paul Baquiast, Alain Cardon, Entre Science et intuition : la conscience artificielle, préface de René Trégouet, sénateur, éd. Automates Intelligent Paris, avril 2003.

    • Jean-Charles Campagne, Alain Cardon, An approach to control multi-agent systems, International IPSI-2003k Conference, 2003.

    • Alain Cardon, Modéliser et concevoir une machine pensante, éd. Automates Intelligents, Paris mars 2003.

    • Jean-Charles Campagne, Alain Cardon, Artificial emotions for robots using massive multi-agent systems, Social Intelligence Design International Conference, 2003.

    • Alain Cardon, Jean-Charles Campagne, Artificial sensations for social autonomous robots, Social Intelligence Design International Conference, 2003.


    • Alain Cardon, Conscience artificielle et systèmes adaptatifs, Editions Eyrolles, Paris, janvier 2000.

  • Team



    • Rostom Cheikh-Aissa
    • Franck Chevallereau
    • Geoffrey Plouvier
    • Guillaume Luccisano
    • Yann Achard
    • Julien Eres
    • Camille Havas
    • Othalia Larue
  • Affiliated Institution

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  • Contact Us

    If you have any question about Artificial Consciousness.