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La simulation agent et des applications
Cour d’introduction général
Juliette Rouchier, GREQAM- CNRS, 2 rue de la Charité, 13236 Marseille Cedex 02, France
rouchier@ehess.univ-mrs.fr
SMA: AEIO model agents
Real or virtual entity, autonomous, local percreption of environment, capable of acting. Shared perception, image of self and others, memory, goals, beliefs.
environnement Objects : caracteristics, dynamic evolution lawsFormalised by a grid (automata) or a network
interactionscommunication groups, language, communication protocols (ie : offer
competencies, answer)Interpretation of messages
organisationCorrelation concerning the evolution of certain entities, temporal
organisation Groups or predefined networks with tasks, shared norms, links for
communication
Complex system (Simon, 61)
Self-organisation, emergence : what wasn’t defined in the individual entities’ behaviour
Traffic jam, adaptation,...
System limits interacting entitiesdynamics controlfeedback
OBSERVED System Points of view
expectations emergence
Artificial: human made, with a goal, imitating, imperative
Black box of a system
Cellular automata
Automata S = set of states I = set of inputs O = set of outputs Transition function
State OutputInputs
Network of automata with special architecture : inputs of some are outputs for others
Intelligence artificielleSystème expert et multi-expertsLogique formelleThéorie de l’information
Cybernétique - contrôleMinskyBateson
Example : Game of life
MANTA : interactions through a resource and specialisation / learning through simple feedbacks
Reactive agents Ants, egg, larva, cocoon
Don’t perceive the others, but stimuli in the environmentCan choose an action, according to activity levels (thresholds)Competing tasks: cure, feed, carry gather food
A. Drogoul
Goal: To represent labour division with very simple agents
Inspiration: ants societies
Build a framework that is useful to computer scientists and ethologists
MANTA : interactions through a resource and specialisation / learning through simple feedbacks
A. Drogoul
• Success of sociogenesis in about 20% cases, without any centralised decision• Collaboration without any knowledge of the others (intelligence of the programmer)• Specialisation without loosing adaptativity when thresholds of reactions are well adapted• Organisation more or less complex and division of labour (different age > different tasks)• Progressive complexification > diverse learning processes – reinforcement engendered by competition
Emergence of hierarchies (Doran, Palmer) Goal: to produce a hierarchical society from an egalitarian
society Hypothesis: the resource characteristic is the explanation
Resources requires many hunters (complexity)defined by: location, instances, energy, type, complexity,
distance for agents to get it, sime be4 renewal
Agents seek to “stay alive” (energy)cognitive and action “if then” rules (1 action per time-
step)memory: resource model, social model, message buffer,
miscellanous (hunger, behaviour mode, perception range)location, speed, sensory range, skill, energy, hunger limitmodes: autonomous, recruiting, biding, executing
Recruitment process: leaders and followers an agent calls up -> the other proposes a bid > accepts the bid >>
groupwhen leader, the agent involves its own group when accepting
(groups get to be parts of groups)Simulation Variables:
recruitment rules, Fidelity (how long it stays in the group after activity stops) conditions of agreement to follow the leader
Observation indicators:depth of hierarchy (individual and global)
Results:Groups appear and last, autonomous actions, if resources are gathered in a place, groups migrate in this area Need of low complexity resource to have step by step hierarchy buildingDecrease of productivity with rigid social structures (fidelity).
Emergence of hierarchies (Doran, Palmer)
Growing artificial societies. Social science from the bottom-up (sugarscape)
Environment – resources Agents : layers building
Needs / satisfaction / perception / movement > migrations, differenciations Reproduction / death > s₫lection Inheritage > less inequalities but less selection
Culture : gene dissociating two groups Fight for access to resource > elimination or assimilation
Exchanges : two resources and different needs With exchanges > reduction of mortality and increasing of inequalities
Economic hypotheses test Equilibrium appear
Pollution, preference evolution, disease transmission in migration
Agents
No learning
Learning
Reactive behavior Cognitive behavior
Perception
Action
Perception
Decision
ActionPerception
Action
Assessment
PerceptionDecisionAction
Assessment
NOT INDEPENDENT !!!
Autonomy vs independenceSeparation agent - environment / ability to adapt in evolving environment
Actions ordering, interpretation, choice, behavioural change
Directe Communication : give informations / distribute tasks / solve conflicts / learn
Representation of others : how, with whom to communicate, what knowledgeRepresentation of the relationship: familiarity, trust Type of language, interpretation of a message
Interactions
Direct Indirect
Modification of representations / beliefsModification of goals
Indirecte Communication: évolve without consciousness of others’ presence but react to the transformation of environment
signals left in environment (stimuli - externalities)
COGNITIVE
REACTIVE
Memory
No memory
Conservation of thresholds
Conservation of messages received
Conservation of messages received and sent
Conservation of large amount of information about the context of the messages
Organisation
Built in organisational elements Networks
who to communicate withdelegationcommitmentdependence – authorityknowledge of abilities
Repartition of tasks – roles - abilitiesTemporal organisation of the systemWho has access to ressource / control overNorms of behaviour that exist in the system – interpretation for agents
Emerging patterns that retroact on the system by constraining agentsCompetitionEmerging norms (regularities of behaviour in the group)
Diverse representations of social behaviour From goals to intentions (commitment)
Blinded : Until the agent believes it has accomplished its intention Single-minded : As long as it thinks it is possible Open minded : As soon as it has the goal
Some strategies for negotiation
Always concede Be competitive Be cooperative : look for a mutually acceptable solution Inaction Break
Reactive agents often coordinate through the environment Two approaches for cognitive agents
define mutual beliefs, joint desires and joint intentions define norms and conventions.
Methods to have agents evolve Reinforcement learning
Utility function > evaluate results and classify them Learning / memory capacity / Change of behavior rules
Comparison and copy of others’ methods Information diffusion or behaviour diffusion Choice of relevant agents to copy (trust, network) Mecanisms to adopt behaviours
Genetic algorithms (population level – social learning) « fitness » function, reproduction, meeting, mutation
Memetics� Inspiration of genetics in building of representation
the noosphère (Morin), « memes » (Dawkins), epidemiology of representations (Sperber).
� Hales (following Bura): « Memes » are on animats using an environment and subject to selection.
Each meme : propensity to mute and to reproduce; fights with neighbors and strenghthening.
3 stages : calculation of satisfaction, mutation, replication.
If satisfied increased aggressiveness and decreased mutation If not satisfied the reverse is trueExistence of a meta-meme: open-mindedness that suppresses that phenomena
Results Scenario : Just enough food, too much food, predators.
Stabilisation of size of animat population able to occupy an area (carrying capacity).
killing memes population can grow but is a sign of instability in the system
open-mindedness meme help global survival of the population
En ce qui concerne les apprentissages individuels, Bourgine [1993] distingue plusieurs niveaux de rationalité des agents selon leur relation à leur environnement et leur capacité à modéliser le réel.
•Les agents réactifs réagissent de manière fixe à l’information provenant de leur environnement, sur le mode stimulus-réponse (réponse sensori-motrice ou " pavlovienne " héritée génétiquement) : il y a absence d’apprentissage. •Les agents hédoniques apprennent (par auto-renforcement) à modifier leur comportement afin d’augmenter leur " plaisir ". Ils sont capables d’anticipations " hédoniques " et d’adaptation lente à partir de leur expérience historique, ce qui suppose un niveau de conscience plus élevé que l’agent réactif (consciousness). •Les agents éductifs sont dotés d’une capacité de modélisation de leur environnement, ce qui suppose la capacité de former des représentations symboliques, de simuler les conséquences d’une action sur leur environnement, et donc un niveau de conscience plus élevé (awareness).
Selon une perspective plus proche des catégories de l’économiste, Walliser [1997] propose une typologie des processus qui permettent de converger vers un équilibre en théorie des jeux. Il en distingue quatre, soit par ordre décroissant des capacités cognitives attribuées aux agents :
•Dans un processus EDUCTIF, chaque joueur dispose d’assez d’information pour simuler parfaitement le comportement des autres joueurs, ce qui conduit immédiatement à l’équilibre : il n’y a pas d’apprentissage.•Dans un apprentissage EPISTEMIQUE, chaque joueur révise ses croyances relatives aux stratégies des autres adversaires à partir des informations qu’il a pu observer (Fudenberg, Levine [1998]).•Dans un apprentissage COMPORTEMENTAL, chaque joueur modifie sa stratégie compte tenu des résultats observés de ses propres actions dans le passé (agent hédonique).•Dans un apprentissage ÉVOLUTIONNAIRE, chaque joueur joue une stratégie fixe qui se reproduit proportionnellement au gain obtenu lors de confrontations aléatoires (agent réactif).
Simulation : temporal evolution
Initial setting Environment, number of agents, dotations, ...
A time-stepenvironment evolves
agents perceiveagents make choice
agents actagents communicate
Final setting
Model (agents, environment, interactions, organisation)Parameters : number of agents, learning principle, costs
Simuler = chercher les « causes » de l’auto-organisation
Observer l’auto-organisation : critère global, critère individuel, aggrégation de données individuels, liens / rencontre des agents, représentations individuelles and collectives.
INDICATEURS PERTINENTS POUR DECRIRE DES PHENOMENES «EMERGENTS »
Analyse est possible à travers la comparaison (= « sensibilité aux changement des valeurs de paramètres ») : changement de la situation initiale ou des règles de l’univers – observation des résultats finaux et des processus intermédiaires
CHERCHER LES IMPLICATIONS DES REGLES ET DE LA SUCCESSION D’EVENEMENTS
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