Model of cognitive agents to simulate complex information systems Alain-Jérôme Fougères UTBM-SeT University of Technology of Belfort-Montbeliard Belfort Technopôle, 90010 BELFORT – France E-mail:
[email protected]
Abstract--To model, to conceive or to simulate complex systems whose components are in strong interactions, the paradigm multiagents seems allowed henceforth. This short article presents a generic approach of the behaviour modelling of a system of assistance to the detection of epidemics where the capacity of decision-making of the components (co-operating agents) can be exploited. The epidemiologists have a certain number of indices which enable them to differentiate, on the basis of accumulation of symptoms on people, the cases isolated from a disease or the consequence of a contagion (epidemic). The system conceived on the basis of F rench organization of the public health, makes it possible to simulate cases of diseases (local or scattered) and to regularly draw up reports of diagnoses of possible epidemics.
Keywords: Knowledge engineering, multi-agent systems, cognitive agents, decision-making aid, Petri nets.
I. INTRODUCTION
The modelling and the simulation of complex and distributed information systems, animated by co-operative, autonomous dynamic processes and able to make decisions, must integrate a representation of knowledge and behaviours, distributed on the various decisional actors. This must make it possible to improve the follow-up of the changes of organization and their impact on the systems considered. The approach multi-agent offers a level of abstraction adapted for the modelling of the dynamics of the complex organizations and the simulation of the changes of organization, with an aim of studying them. Indeed, the multi-agent systems make it possible to coordinate the behaviour of intelligent agents interacting in a company to carry out tasks or to solve problems [6, 19]. After having worked on the organizational aspect of multi-agent system (MAS) to simulate the reorganization of flexible manufacturing system or the regulation of urban transport system, we concentrated on the modelling of agents known as "cognitive", having real capacities of communication and being able to be used as a basis for the design of assistance system for users. Our reflexions were enriched by the models suggested in the field of Human-Computer Interaction, and in particular on the recent approach called cognitive engineering. The models of interaction between computer system and user enabled us to define the interaction between software agents having cognitive capacities. To illustrate our approach, we will concentrate on the aspects related to the interactions between the various decisional actors of complex information system such a system of detection of epidemics. For that, we formalize the agents with Petri nets. Petri nets, which was already presented like a very abstract model for the MAS [12], make it possible to represent all the dynamic components of the complex systems. They are thus well adapted to visualize and simulate the behaviour of the agent themselves and a fortiori the agents systems. Our presentation is structured as follows: in section 2 we describe agent modelling of complex information system, namely a systemic modelling of the organization, facilitating cohesion between the decisional structure and the physical structure, and the description of the elements of the architecture of the cognitive agents by using Petri nets. Section 3 describes the system of detection of epidemics, as well as the system of simulation of these epidemics. Finally, in section 4, we give some conclusions on our approach of modelling, then we evoke the prolongations of our work. II. MODELLING OF COMPLEX INFORMATION SYSTEM A. A systemic modelling We propose in this section to define a strategy of organization of complex systems centred on the concept of reactivity. This need for reactivity comes from a new vision of the organizations in which the actors see themselves increasing their degree of autonomy and flexibility (distribution of the decision-making and bringing together between the decisional structure and the physical structure). Then, the autonomy of the actors of an organization makes it possible to develop locally processes of effective adaptation. Systemic modelling proposed by Jean-Louis Ermine [5] inspired our reflexion. It takes again the traditional diagram OID (Operations, Information-memorizing and Decision) to which it integrates a fourth system allowing the knowledge circulation.
Flow of cognition
Decision system
Information system
Knowledge system
Operative system
Flow of competence
Figure 1. Four components of the systemic modelling of knowledge systems. B. The modelling of the cognitive agents One of the principal interests of the MAS is that they make it possible to distribute agents, entities communicating, autonomous, reactive and equipped with competences [6, 19]. To carry out a MAS according to these criteria, it is necessary to equip each agent, known as "cognitive", of the three following properties: 1. 2. 3.
Independence: an agent, able to achieve convergent tasks, must have its own set of resources and knowledge. Communication: agents which collaborate must be able to communicate to exchange information. Intelligence: the agents are defined goals according to their competences, they must thus have knowledge and mechanisms to reason with this knowledge (their expertise and their know-how).
To model such agents we should define their architecture (cognitive functions and interactions), as well as the knowledge structuration necessary for their various activities. 1) Elements of modelling The definitions which we adopt to distinguish the concepts of reactive agents and agents cognitive are established starting from the operator model of Jens Rasmussen [18] (figure 2). Thus, our agents are neither cognitive, nor reactive, they have behaviors adapted to the tasks which they carry out. 1. 2.
Def SMA ::= Def Agent ::=
Goals Knowledge-based behaviour
Identification/ interpretation
Decision of task
Planning
Rule-based behaviour
Recognition
Association state/task
Procedure
Skill-based behaviour
Observation
Execution sign reflex
Informations
Actions
Figure 2. Rasmussen’s model of the operator: three levels of behaviour [17]
We proposed a general architecture of a cognitive agent respecting the three properties of independence, communication and intelligence [7]. This one (figure 3), inspired by the theories of modularity of Jerry Fodor [9] and by the decomposition in central module and peripheral module, is made up of five modules managing the memory, the perception, the communication, the control and the agent reasoning:
Environment
Society of agents
Interactions
Perception
Communication
Interpretations
Memory Control goals
Competences
management
Domain knowledge Accointances In,tentions
Decision reasoning
Figure 3. Modular architecture of a cognitive agent
1.
2.
The module of perception: it is the process allowing acquisition of knowledge on the environment (data and variables) in which the agent evolves. The operation of this module corresponds to the first two phases of the cognitive process model: . The observation within the framework of the acquisition of information is carried out two modes: an intentional mode in the case of follow-up of goals and the attentive mode when the agent is available. In the absolute, the interpretation of information should be associated with the checking of the relevance of information. The module of communication: it is the mechanism of interactions of the cognitive agent with the community of agents. We insist on the fact that to qualify intelligent agents it is essential to prove their capacity to communicate in an individual or collective step (figure 4). The module of communication receives messages, interprets them and can transmit some on decision of the control module. To communicate with the other agents and to express its intentions, like defined in the theory of the acts of language [2], agents use a communication protocol based on language KQML (Knowledge Query and Manipulation Language) [8]. The general format of an act of communication is described by the quintuplet . Within the framework of our work on the improvement of capacities of communication of the agents, we are defining a core of constrained natural language [10]. This language will be used by agents defined for a system tutorial [11] and will offer prospects for projects of strong assistance to the user (tools of assistance tools, man-machine communication).
Interaction
cognitive agent social faculties Communication
Cooperation
Figure 4. The triangle of interaction between agents 3.
4.
The module of control: it manages the set of modules of the cognitive agent using knowledge on the synchronization and on the control of the internal tasks (for example analysis of messages) and of the external tasks concerning collaboration with other agents. The treatment of information or received messages breaks up into four phases: the formalization of information, the determination of the tasks to be realized, the transmission of the possible actions to solve these tasks with the module of reasoning, and the transmission of the response (decision) to the module of communication. The module of control associated with the module of decision forms the central system of the agent; other modules constituting the peripheral system. The module of decision: in fact the process makes it possible the agent to reason to make decisions in an autonomous way. According to the knowledge contained in the cognitive module (competences, intentions, rationalities, beliefs,
5.
acquaintances) and to the type of decisions to be taken, the module of decision deduces sequences of actions which it must transmit to the module of control. It acts in fact, following a phase of observation then to an evaluation of the situation (fusion of information), to make a decision on the future actions has to carry out. This corresponds to the two last phases of the cognitive process model exposed for the module of perception. The models selected to specify these tasks of decision-making (ie. problems solving) are, on the one hand that of the scale of decision [17] in the attentive mode, and on the other hand that of the theory of the action [14] in the intentional mode related to the follow-up of goals. The module of memory: this module is characterized by accountancies (knowledge on the other agents of the system), by competences (set of knowledge on the procedures and the tasks that can achieve an agent, described with the method MAD [20]), as by the intentions/rationalities which correspond to the personal motivations of the agent (intentions) and to its modes of evaluation (rationalities). The use of knowledge by an agent is done on the principle of model ACT (figure 5), characterized in particular by the differentiation between a permanent memory (long-term memory) and a working memory (short-term memory). The formal representation and the acquisition of knowledge are described in the following section (cf II.B.3).
long-term memory
Declarative Memory
KB
matching
stockage
Working Memory
short-term memory recovery
environment
Procedural Memory
execution
Control Module
Figure 5. Model of memory according from ACT model [1]
We will add to this description that the agents are heterogeneous entities with varied modes of interactions and complex behaviours. A modelling of MAS must be able to define the type of organization of the agents and the capacity of evolution of the latter. 2) Petri nets model for the cognitive agent To facilitate the modelling and the analysis of complex systems, we developed in the form of Petri nets a set of 5 generic modules, allowing to establish the model of the cognitive agents presented above. Figure 6 presents the Petri net complementary to the five precedents, namely the cycle of operations of a cognitive agent. A cycle alternates two modes on periodic request of the module of control: the continuation of activities according to the goal that the agent was fixed, or the acquisition of information by the observation of the environment (module of perception) and by the consultation of its message-box (module of communication).
Figure 6. Basic behaviour of a cognitive agent
3) The construction of the knowledge base of the agents We have just stated that the general architecture of a cognitive agent is made up of five modules managing knowledge, perception, communication, control and reasoning of the agent. In a more precise way, the cognitive module contains the whole of specific knowledge to each agent: the accountancies (knowledge on the other agents), competences (knowledge on the rules of operation and the state of the system), as well as the intentions (personal motivations of the agent). We describe below the various phases of development of the knowledge base of each agent, as we already tried out for the acquisition of the expertise trade of the agent tutor of an intelligent tutorial system exploited in professional environment [11]. Knowledge of an agent is represented by relational structures of objects, actions and decisions (in the form of frames) and by rules of inference. To carry out this conceptualization we need two types of knowledge: terminological knowledge indicating the objects to be considered, and assertional knowledge, elaborate from the analysis of the discussions with an expert. To direct this conceptual phase of modelling we followed the MKSM method [5] (Fig. 6). As for the modelling of knowledge it respects method KOD [21]. This one proposes three models: the practical model, the cognitive model and the processing model. Here, we consider only the first both model, the last being related to the implementation. The practical model is the representation of statements in natural language by means of elements called taxa (identification of the objects), acta (identification of the actions) and schema (identification of the rules of inference). The cognitive model is an abstraction of the practical model. It proposes a representation of the mental process (of an expert for example) by structuring the taxa in taxonomy, the acta in actinomy and the schema in diagrams of interpretation.
texts/tape recorder
Expertise collection
S1
tools for structuring aid . MKSM knowledge . KOD knowledge Knowledge identification S2
Expert
Knowledge modelling
S3
Knowledge engineer
... Validation {frames, assertions, rules}
Figure 7. Methodology for collection and structuring of agents knowledge
III. CASE STUDY: A SYSTEM OF EPIDEMICS DETECTION The total system is described by the preceding figure. It is composed of 3 subsystems: a multi-agents system (MAS) for the simulation of the epidemics, a MAS for the detection of possible epidemics and a decision-making system which exploits medical knowledge allowing to diagnose at the same time diseases and epidemics. Each category of agents of the detection
system of epidemics have its own knowledge to establish the diagnostic ones in relation to the roles which they play within the organization and of their environment. The decision-making is thus distributed. The assistance system thus has the role to assist the agents on their initiative and to take part to the process of memorization.
Reactive MAS: simulation of epidemics
Cognitive MAS: detection of epidemics Doctors network Améd
DDASS
Arég
KB Aloc memory Anat
Améd Améd
Aloc Arég
Améd Améd
InVS
Aloc
Améd Améd
DRASS
Knowledge Based System: epidemiology
Aloc
Assistance system to detection
Figure 8. Architecture of the system of epidemics detection A. The MAS of epidemics simulation This reactive MAS, related with the systems developed in the projects of artificial life [3, 16], is composed of two types of reactive agents: 1.
2.
The agents contaminator (Acont). They are agents of infections, carriers of the diseases recognized by the system, in particular the notifiable diseases [22], which will be the vectors of the contamination. They disappear when the transmission with an agent individual is carried out. The agents individual (Aind) which can be either healthy, or contaminated. In this last case they consult their doctor.
An agent contaminator is introduced locally by a simulation agent which activates the diseases (influenza, meningitis, whooping-cough, etc.). This agent contaminator can, because of its proximity, infected an individual agent healthy. This last becomes carrying the disease and can convey it in its evolutionary environment (displacements). B. The MAS of epidemics detection This MAS of detection and alarm which must allow a fast reaction of the medical authorities, applies the French organization of the public health system. It includes 4 levels of cognitive agents (actors), from which the roles are distinct: 1.
2.
3.
4.
The agents doctors (Améd), made up in networks (in particular the national network doctors sentinels), represent the first level of the system of health in direct relation with the patients. They have as a role to count symptoms, to diagnose diseases and to collect information to be communicated to agents DDASS. The agents DDASS (Aloc and Ains, Departmental Direction of Medical and Social Action) have functions of consultation, council, control and communication with the doctors and the laboratories. Their mission is to provide a diagnosis help for the doctors, an increase of information to the regional or national authorities and an implementation of the national directives for the fight or the prevention of diseases. The agents DRASS (Areg, Regional Direction of Medical and Social Action) are agents which centralize regional information. They transmit information to the InVS agent or transmit medical decisions to agents DDASS. This level however has a role limited in the organization, for it is often shorted-circuit. The national agents whose agent InVS (Anat, National Institute of Medical Supervision, the national organization of supervision of the health of the population) collect information to ensure a role of control/decision on the French national territory or to establish statistical or qualitative conclusions/reports on epidemiologic phenomena.
Each agent is able to make a decision locally and can ask to be assisted by the assistance system of detection.
In this moment we think of the extension of this organization to the other actors of the medical system implied in the chain of fight against the diseases (laboratories and in particular the National Center of Reference, Institut Pasteur, security services of water,…). IV. CONCLUSION We have presented a generic framework of modelling of cognitive agents defined with an aim of distributing the decisionmaking in complex systems, to improve the performances of them. The correlated formal approach consists in defining a modular architecture for the cognitive agents and using Petri nets to specify their behaviours [12]. The major advantages which arise from the use of the Petri nets are, on the one hand, the possibility of giving at the same time formal and graphic specifications our agents, and on the other hand, the possibility of simulating the system before its execution, then to evaluate part of these properties. We illustrated our approach on the definition of a system of simulation and detection of epidemics which combines complexity (3 subsystems of distinct design to integrate) and clearness of presentation. The development of a methodology of formal definition such intelligent systems, dedicated to the modelling and the simulation of complex systems, constitutes our prospects for research. Moreover, we continue to reflect to refine our models of description of the individual and collective behaviours of the agents in order to specify the level of autonomy of those. Our modelling of the degree of attention/intention is still rather elementary, we reflect on the possibilities of weighting of the latter to improve the performances of our agents. V. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]
ANDERSON J.R., The architecture of cognition. Cambridge: Harvard University Press, 1983. COHEN P.R. and LEVESQUE H.J. Intention is Choice with Commitment. Artificial Intelligence, 42, p.213-261, 1990. DROGOUL A. and HUTZLER G. Le jardin des hasards : peinture abstraite et I.A.D. réactive, Rapport de recherche LAFORIA, 96/04, 1996. EL FALLAH-SEGHROUCHNI A., HADDAD S. and MAZOUZI H., Protocol Engineering for Multi-agent Interaction, Proceedings of MAAMAW’99, Valencia, Spain, June 1999. ERMINE J.-L. Les systèmes de connaissances, 2è éditions, Hermès Science Publications, Paris, 2000. FERBER J. Les systèmes multi-agents : un aperçu général, Technique et Science Informatiques, 16(8), 979-1012, 1997. FERBER J. and GUTKNECHT O., A meta-model for the analysis and design of organizations in multi-agent systems, Proceedings of ICMAS'98, 1998. FININ, T., FRITZSON, R., MCKAY, D., MCENTIRE, R.: KQML as an agent communication language, Proceedings of CIKM’94, ACM Press, 1994. FODOR, J. The Modularity of the Mind, Cambridge, Mass, MIT Press, 1983. FOUGERES A.-J. Formal specifications building from specifications written in natural langage. Proceedings of HCP´99 (Human Centered Processes), Brest, France, 1999. FOUGÈRES, A.-J. Un système tutoriel intelligent adapté à la formation de régulateurs de trafic, Sciences et techniques éducatives, Vol. 8 – n° 1-2, pp 141-147, 2001. HOLVOET T. Agents and Petri Nets, in O. Herzog, W. Reisig, and R. Valk, editors, Petri Net Newsletters3, number 49, 1995. JENNINGS N.R. and WOOLDRIDGE M.J. Applications of intelligent agents, in Springer-Verlag (Ed.), Agent Technology: Foundations, Applications and Markets, 1998. NORMAN D. A. Cognitive engineering, in User centred system design: nez perspectves on human computer interaction, D.A. Norman & S.W. Draper (Eds), Hillsdale, NJ: Erlbaum, 1986. OCELLO M., KONING J.-L. and BAEIJS C. Conception de systèmes multi-agents : quelques éléments de réflexion méthodologiques, Technique et Science Informatiques, 20(2), 233-263, 2001. QUINQUETON J. and HAMADI Y. Communication et Emergence : une épidémie chez les termites, JFIADSMA’99, 1999. RASMUSSEN J. The human as a system component, in Human Interaction with Computer, H.T. Smith & T.R. Green (Eds), London Academic Press, 1980. RASMUSSEN J. Skills, rules, and knowledge ; signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, 257-266, 1983. SHOHAM Y. Agent Oriented Programming, Artificial Intelligence, 60(1), p. 51-92, 1993. SCAPIN D., PIRRET-GOLBREICH C., Towards a method for task description: Mad, in Work in display units 89, L. Berlinguer & D. Berthelette (Eds), Elsevier Science Publishers, North Holland, 1990. Vogel C. Genie Cognitif. Masson, Paris, 1988. Note de la direction générale de la santé sur la transmission de données individuelles à l’autorité sanitaire, BEH, Bulletin épidémiologique hebdomadaire, n° 47, 23 novembre 1999.
