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 region of analysis and improvement concerning man made intelligence, machine technology, software program engineering, and developmental biology, in addition to cognitive and social science.
This booklet 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 means of best researchers in particular solicited for this e-book. The articles are geared up into topical sections on
- studying, cooperation, and communication
- emergence and evolution in multi-agent systems
- theoretical foundations of adaptive agents
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Additional info for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
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. Leslie Pack Kaelbling, Michael Littman, and Andrew W. Moore.
Longer sequences provide more reliable estimates. To reason about the true expected payoff, we must make some assumptions about the possible form of the stochastic payoff for each joint action: for example it must have finite variance. Here we use a Gaussian model and estimate its mean and variance from the observations. If n payoffs are observed with empirical average m and sum of squares S, we obtain estimates for the population mean µ and its variance σµ : µ ˆ=m S + σ02 m2 − 2 n n σ0 is a parameter to the algorithm and should be based on the expected variance of payoffs in the game; in all our experiments σ0 = 10.
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. When k = 0, the reason for the baseline experiment’s failure is not the existence of a miscoordination penalty. Instead, it is the existence of multiple optimal joint actions that causes the agents to converge to the optimal joint action so 26 S. Kapetanakis, D. A. Strens infrequently.
Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning by Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)