Why interaction is more powerful than algorithms
Communications of the ACM
ACM Computing Surveys (CSUR)
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Resource-aware exploration of the emergent dynamics of simulated systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Towards a Methodology for Engineering Self-Organising Emergent Systems
Proceedings of the 2005 conference on Self-Organization and Autonomic Informatics (I)
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
Enhancing self-organising emergent systems design with simulation
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
Automatic tuning of agent-based models using genetic algorithms
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Massive multi-agent systems control
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
Using the experimental method to produce reliable self-organised systems
Engineering Self-Organising Systems
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Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.