Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Intelligence by design: principles of modularity and coordination for engineering complex adaptive agents
Interac-DEC-MDP: Towards the Use of Interactions in DEC-MDP
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Classifier fitness based on accuracy
Evolutionary Computation
Enhancing self-organising emergent systems design with simulation
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
Towards an emergent taxonomy approach for adaptive profiling
SOCASE'08 Proceedings of the 2008 AAMAS international conference on Service-oriented computing: agents, semantics, and engineering
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Systems plunged into dynamic environments need evolving behaviours in order to self-adapt to these changes. These behaviours cannot be predetermined because it is impossible to list exhaustively all the situations the system may be faced with. Therefore, it becomes necessary to define real time algorithms that enable systems to autonomously adapt their behaviours to the current context. This paper focuses on behavioural rules learning. We propose, in that sense, a self-organisational approach based on local cooperative criteria that enable to discover triggering conditions of behavioural rules. Even if our approach intends to be generic, the principles and the evaluations have been defined in order to construct a system that enables the creation and the dynamic update of user profiles.