Exploiting constraints in design synthesis
Exploiting constraints in design synthesis
Evolving algebras 1993: Lipari guide
Specification and validation methods
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
From Active Objects to Autonomous Agents
IEEE Concurrency
Machine Learning
Semantical consideration on floyo-hoare logic
SFCS '76 Proceedings of the 17th Annual Symposium on Foundations of Computer Science
Situation recognition: representation and algorithms
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Learning User Preferences in Multi-agent System
CEEMAS '01 Revised Papers from the Second International Workshop of Central and Eastern Europe on Multi-Agent Systems: From Theory to Practice in Multi-Agent Systems
Generic Command Interpretation Algorithms for Conversational Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Generic command interpretation algorithms for conversational agents
Web Intelligence and Agent Systems
Influence of Personality Traits on the Rational Process of Cognitive Agents
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Influence of FFM/NEO PI-R personality traits on the rational process of autonomous agents
Web Intelligence and Agent Systems
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In this paper, we propose a reasoning model for extracting collective behaviours in Multi-Agent Systems (MAS) from regularities in interaction streams. Using reflexive structures to describe the actions and knowledge of the system, called views, we build chronicles that catch and register the actual situations related to events and actions occurrences. These chronicles are then intensionalised in order to extract regularities in a single autonomous agent's behaviour, thus defining a local behaviour. We then propose to extend this model for extracting collective behaviours in MAS using machine learning algorithms. Finally, we try to show that this dynamic analysis of an agent's runtime can lead to organisations in MAS.