By Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)
Adaptive brokers and Multi-Agent structures is an rising and fascinating interdisciplinary quarter of analysis and improvement regarding synthetic intelligence, desktop technological know-how, software program engineering, and developmental biology, in addition to cognitive and social science.
This ebook surveys the cutting-edge during this rising box by means of drawing jointly completely chosen reviewed papers from comparable workshops; in addition to papers by way of prime researchers in particular solicited for this ebook. The articles are prepared into topical sections on
- studying, cooperation, and communication
- emergence and evolution in multi-agent systems
- theoretical foundations of adaptive agents
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Extra info for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
Convergence results for single-step on-policy reinforcement-learning algorithms. Machine Learning Journal, 38(3):287–308, 2000. 9. Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the Tenth International Conference on Machine Learning, pages 330–337, 1993. 10. C. J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England, 1989. 11. Gerhard Weiss. Learning to coordinate actions in multi-agent systems. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, volume 1, pages 311–316.
For a given max moves, we experimented with a variety of s, max temp combinations and found that they didn’t have a significant impact on the learning in the baseline experiments. Their impact is more significant when using the FMQ heuristic. This is because setting max temp at a very high value means that the agent makes random moves in the initial part of the experiment. e. moves based on the estimated value of its actions) when the temperature has become low enough to allow variations in the estimated value of an action to have an impact on the probability of selecting that action.
J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England, 1989. 11. Gerhard Weiss. Learning to coordinate actions in multi-agent systems. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, volume 1, pages 311–316. , 1993. Cooperative Learning Using Advice Exchange 1,2 Luís Nunes and Eugénio Oliveira 1 1 Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC) – Núcleo de Inteligência Artificial Distribuída e Robótica (NIAD&R), Faculdade de Engenharia da Universidade do Porto (FEUP),Av.