By Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)
Adaptive brokers and Multi-Agent structures is an rising and interesting interdisciplinary region of analysis and improvement concerning man made intelligence, laptop technological know-how, software program engineering, and developmental biology, in addition to cognitive and social science.
This booklet surveys the cutting-edge during this rising box through drawing jointly completely chosen reviewed papers from comparable workshops; in addition to papers by way of top researchers in particular solicited for this e-book. The articles are prepared into topical sections on
- studying, cooperation, and communication
- emergence and evolution in multi-agent systems
- theoretical foundations of adaptive agents
Read Online or Download Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning PDF
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Extra info for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
However, the performance of the agents depends not only on the size of the penalty k but also on whether the agents manage to agree on which optimal joint action to choose. Figure 2 depicts the performance of the learners for k = 0 for the baseline experiments and with c = 1 for the FMQ heuristic. 2 0 500 750 1000 1250 number of iterations 1500 1750 2000 Fig. 2. Likelihood of convergence to the optimal joint action in the penalty game k = 0 (averaged over 1000 trials). As shown in Figure 2, the performance of the FMQ heuristic is much better than the baseline experiment.
Sequential optimality and coordination in multiagent systems. In Proceedings of the Sixteenth International Joint Conference on Articial Intelligence (IJCAI-99), pages 478–485, 1999. 2. Caroline Claus and Craig Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Articial Intelligence, pages 746–752, 1998. 3. Drew Fudenberg and David K. Levine. The Theory of Learning in Games. MIT Press, Cambridge, MA, 1998. 4.
In Proceedings of the Fifteenth National Conference on Articial Intelligence, pages 746–752, 1998. 3. Drew Fudenberg and David K. Levine. The Theory of Learning in Games. MIT Press, Cambridge, MA, 1998. 4. Leslie Pack Kaelbling, Michael Littman, and Andrew W. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 1996. 5. Martin Lauer and Martin Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In Proceedings of the Seventeenth International Conference in Machine Learning, 2000.
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