1. 1 La théorie AMAS 1 Conception de systèmes complexes basés sur des mécanismes d’auto-organisation coopératifs IRIT - Institut de Recherche en Informatique de Toulouse France Equipe SMAC– www.irit.fr/SMAC Jean-Pierre Georgé george@irit.fr 1 AMAS : Adaptive Multi-Agent Systems

2. 2 Outline  MAS Background  Some applications  Emergence and Self-Organisation in Artificial Systems  The AMAS theory Functional adequacy theorem Self-organization Cooperation ADELFE Example  Conclusion

3. 3 Multi-Agent Systems (1/2)  Definition SMA = macro system composed of autonomous agents which interact in a common environment in order to solve a common task SMA = result of the organisation between agents  Characteristics Autonomy Distribution Locality Asynchronism

4. 4 Multi-Agent Systems (2/2)  Properties Open / Close Heterogeneous / homogeneous …  Advantages Reusability Modularity Robustness Simplified learning and validation Incomplete specification

5. 5 Agent (1/4)  Autonomous Agent is a physical or virtual entity Autonomous Situated into an environment and able to act in/on it Able to communicate with others agents Having an individual objective / satisfaction function Having resources Able to perceive its environment Having a partial representation of its environment Having skills and offering services Behaviour = consequence of its perceptions, knowledge, beliefs, skills, intentions, interactions…

6. 6 Agent (2/4)  Intelligent Agent Agent situated into an environment able to realize flexible and autonomous actions in order to reach its objectives (Wooldridge 2001)  Autonomous Its being is independent of the being of other agents Agent can maintain its viability in dynamic environment The decision making about its behaviour is done by the agent without external control  Reactive Maintain interactions with its environment Answer to changes into the environment in order to satisfy its goal  Pro-active Generate and reach goals Not only driven by events Take initiatives in order to satisfy its goals  Social Capability to interact wit other agents with communication languages (Jennings, Wooldridge, 1995)

7. 7 Agent (3/4)  Intention Status the agent wants to reach, enable to decide the behaviour and to choose a goal  Roles constraint from the collective towards the agent and linked to oan organisation oa structuring of the collective  Skills Distributed by the designer Distribution cannot change during the system life Dynamic distribution  Social Attitudes benevolent altruistic / selfish genuine / liar

8. 8 Agent (4/4)  Life cycle 2 - Decide 3 - Act 1 - Perceive

9. 9 Outline  MAS Background  Some applications  Emergence and Self-Organisation in Artificial Systems  The AMAS theory Functional adequacy theorem Self-organization Cooperation ADELFE Example  Conclusion

10. 10 Optimized Ants Foraging Topin X., Fourcassié V., Gleizes M-P., Théraulaz G., Régis C., Glize P. in Proceedings on European Conference on Cognitive Science 1999

11. 11 SYNAMEC : Mechanical Systems Design  Search the best alternative and the sizes  Search the best topology

12. 12

13. 13 BIO-S  Several atoms, no environment  Find the conformation which minimizes the energy of the molecule  Besse and all, 2000

14. 14 Carrier Robot  Several robots  2 Rooms + Narrow Corridors + Boxes  Resource Transportation task from one room towards the other room Pick up one box in one room and drop it in the other room Picard, 2000, ESAW 2004

15. 15 Outline  MAS Background  Some applications  Emergence and Self-Organization in Artificial Systems  The AMAS theory Functional adequacy theorem Self-organization Cooperation ADELFE Example  Conclusion

16. 16 Problems Characteristics Dynamic Environment RoboCup Open System Market place on the Internet Physically distributed Trafic Road Control No known algorithm Ant Hill Management logically distributed Experts Cooperation

17. 17 Difficulties for Designing  Complex systems No global control Designer cannot control and build all the systems  Dynamic of the environment Adaptation  Open systems Adaptation Robustness  Autonomy and adaptation are needed  Inspiration from natural systems  self-organisation Where the adequate function of the system emerges How to design self-organizing system which is functionally adequate? What is the mechanism of self-organization?

18. 18 Why Self-Organization?  Agents must have Local rules Local perceptions  Self-organisation is the mechanism or the process enabling a system to change its organisation without explicit external control during its execution time [DiMarzo, Gleizes, Karageorgos TFGSO 2005]  Find a solution = find the right organisation  Problem Solving: Agents interact and evolve in a common environment … Solving process = succession of organizations (Edmonds, 2005) (Living Design in Picard 2003)

19. 19 Self-Organization in artificial systems  Define local mechanisms  No information on "how" to realize the global function  Feedback and adaptation  Emergence in artificial systems : Object : the global function of the system is emergent Condition : the implementation of the system is not explicitly guided by the knowledge of how to reach the global function  Example in carrier robot application Avoiding each other is not emergent Traffic direction is emergent  not coded in robots and cannot be deduced from the code

20. 20 Outline  MAS Background  Some applications  Emergence and Self-Organisation in Artificial Systems  The AMAS theory Functional adequacy theorem Self-organization Cooperation ADELFE Example  Conclusion

21. 21 AMAS : Adaptive Multi-Agent Systems www.irit.fr/SMAC  Adaptive artificial complex systems design  Adequate function = what the system has to do to be « useful »  Global function realized = result of the organizational process between agents  Change the organization  change the global function  Mechanism used to change the organization : self-organization  Autonomous parts + local rules  Local criterion : cooperation.

22. 22 Functional Adequacy Theorem (Glize, 2000) For any functionally adequate system in a given environment, there is a system having a cooperative internal medium which realises an equivalent function Functionally Adequate Systems Cooperative Systems Cooperative Internal Medium Systems Cooperative Antinomic Indifferent (Galliers, 1992 Ferber, 1995 Glize 1998)

23. 23 Adaptive MAS theory: Hypothesis S system plunged into an environment S realises a function fs S composed of interacting agents Each agent realises a partial function Organisation of S => result

24. 24 Principle of Self-Organisation in AMAS Environment System   Perception   Time t+1 : f*s + Action  Time t : fs

25. 25 Emergent Programing [Georgé 2005] Simple example : 5 agents : +, *, 3 constants A B * + C 2 10 100 20 120 OUTPUT * + B 2 100 10 200 210 OUTPUT A C

26. 26 AMAS Theory: Non Cooperative Situations (1/2) (Capera, 2003)  ANTICIPATION: try to avoid “problems”  EXCEPTION TREATEMENTS : “detection and handler execution”  An agent must have a cooperative attitude It detects and repairs Non Cooperative Situations It tries to avoid Non Cooperative Situations which can be anticipated by itself It always tries to be cooperative BUT an agent is benevolent and not altruistic  sometimes Non Cooperative Situations have to occur

27. 27 Non Cooperative Situations (2/2) Definition of a cooperative situation from the local point of view of an agent  All perceived signals must be understood without ambiguity Incomprehension Ambiguity  The received information is useful for the agent’s reasoning Uselessness  Reasoning leads to useful actions towards others Conflicts Concurrency

28. 28 AMAS Theory: Cooperative Agent  Is autonomous  Respects the criteria of locality  Ignores the global function of the system Fundamental activities : perceive, decide and act in the world  a cooperative situation  realises its function  an uncooperative situation (failure)  acts to come back in a cooperative state

29. 29 ADELFE Process Preliminary Requirements Final Requirements AnalysisDesign Define user requirements Validate user requirements Define consensual requirements Establish keywords set Extract limits and constraints Characterize environment  Determine entities  Define context  Characterize the environment Determine use cases  Draw up an inventory of the use cases  Identify cooperation failures  Elaborate sequence diagrams Elaborate UI prototypes Validate UI prototypes Analyze the domain  Identify classes  Study interclass relationships  Construct the preliminary class diagrams Verify the AMAS adequacy  Verify the global level AMAS adequacy  Verify the local level AMAS adequacy Identify agents  Study entities in the domain context  Identify the potentially cooperative entities  Determine agents Study interactions between entities  Study the active-passive entities relationships  Study the active entities relationships  Study agents relationships Study the detailed architecture and the multi-agent model  Determine packages  Determine classes  Use design-patterns  Elaborate component and class diagrams Study the interaction language Design an agent  Define its skills  Define its aptitudes  Define its interaction language  Define its world representation  Define its Non Cooperative Situations Fast prototyping Complete design diagrams  Enhance design diagrams  Design dynamic behaviours

30. 30 ADELFE Tools  Software to verify the AMAS adequacy  Adelfe Toolkit

31. 31 Non Cooperative Situation Two choices:  Follow the pheromone track  Go towards new foods  To avoid concurrency, the robot- ant go towards new food location even if the pheromone is in a big quantity

32. 32 Non Cooperative Situation Two choices:  Go towards new foods already found by others ants  Go towards new foods unused  To avoid concurrency, the robot- ant go towards the unexploited food location even if there is less food than at the other location

33. 33 Cooperative Attitude Two choices:  Go directly towards the nest  Go directly towards the nest in dropping pheromone  It is a spontaneous communication: the robot-ant drops more pheromone when coming back NEST

34. 34 Oecophylles Ants / Robots-ants

35. 35 Oecophylles Ants / Robots-Ants

36. 36 Outline  MAS Background  Some applications  Emergence and Self-Organisation in Artificial Systems  The AMAS theory Functional adequacy theorem Self-organization Cooperation ADELFE Example  Conclusion

37. 37 Some Applications of AMAS theory  Optimized ant foraging (National project GIS, 98-99)  Flood forecast (generic river behavior model built without geo-physical knowledge) (Georgé, AISB 2003)  Autonomous mechanical design (Capera, Journal of Applied IA 2004)  Emergent Programming (instruction agents) (Georgé, CEEMAS 2005)  Bioinformatics (Mano, EUMAS 2005)  Timetabling (Picard, CEEMAS 2005)  Manufacturing control (Capera, ROADEF 2006)  Dynamic ontologies / adaptive profiling (Ottens, EGC 2006)

38. 38 Conclusion : Theoretical point of view  Cooperation = generic local guide (micro-level)  really emergent global function  Reasoning on local cooperation seems always relevant  Openness: apparition and disappearing of components are possible  No theoretical demonstration of convergence but great reliability in practice  Dynamic equilibrium: Organisation regulation: increasing/decreasing influences or interactions between agents Agent adaptation: modification of local function according to the coupling with its neighbours Population dynamics: creation/disappearance of agents

39. 39 Conclusion : Engineering point of view  No final search state  continuous ability to adapt  Robustness : graceful degradation, no external control and no internal entity centralizing information or decision  “Any time” property: more or less satisfactory solution according to the time devoted to the self- organizing process  Cooperative behaviour leads to a drastic reduction of the search space compared to global search algorithms  Difficulty: exhaustive detection of NCS and relevant treatment (possibility of sub-optimal functioning, requires simulations to test behaviour)

40. 40 References – Web Sites  Santa Fe Institute - Network Dynamics  Réseau d’équipes “Emergent Behaviour Computing” en Angleterre  New England Complex Systems Institute  Université Libre de Bruxelles (CENOLI, …)  CALResCo : The Complexity & Artificial Life Research Concept for Self- Organizing Systems  www.irit.fr/TFGSO www.irit.fr/TFGSO  www.irit.fr/COLLINE

41. 41 References  Bourjot, Christine and Chevrier, Vincent and Thomas, Vincent. A new swarm mechanism based on social spiders colonies: from web weaving to region detection. in Web Intelligence and Agent Systems: An International Journal - Vol 1, N.1, pp47-64 WIAS. 2003  Demazeau Yves Editeur du livre Observatoire Français des Techniques Avancées : Systèmes Multi-Agents, Série ARAGO 29, 2004  Georgé JP., « L’émergence », Rapport interne IRIT n° 2003-12-R, 2003  Georgé J-P., « Résolution de problèmes par émergence, Etude d’un Environnement de Programmation Emergente », Thèse de l’Université Paul Sabatier, Toulouse, Juillet 2004  M. R. Jean : Nom collectif pour Batard, Brassac, Delépine, Gleizes, Glize, Labbani, Lenay, Marcenac, Magnin, Müller, Pesty, Quinqueton, Vidal, « Emergence et SMA », Actes des cinquièmes journées francophones IAD&SMA, La Colle sur Editions Hermès, pages 323- 341, 1997  Mano JP Séminaire Self-organization in Natural System lors du Technical Forum Group on sel-organization in MAS Rome Juillet 2004  M. R. Jean : Nom collectif pour Batard, Brassac, Delépine, Gleizes, Glize, Labbani, Lenay, Marcenac, Magnin, Müller, Pesty, Quinqueton, Vidal, « Emergence et SMA », Actes des cinquièmes journées francophones IAD&SMA, La Colle sur Editions Hermès, pages 323-341

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