Adaptive Agents and Multi-Agent Systems: Adaptation and by Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, PDF

By Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)

ISBN-10: 3540400680

ISBN-13: 9783540400684

Adaptive brokers and Multi-Agent platforms is an rising and interesting interdisciplinary sector of study and improvement related to man made intelligence, computing device technology, software program engineering, and developmental biology, in addition to cognitive and social science.

This e-book surveys the state-of-the-art during this rising box by way of drawing jointly completely chosen reviewed papers from comparable workshops; in addition to papers via major 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

Example text

This is not just in terms of the temperature function but, more importantly, in terms of the action selection strategy. More specifically, it turns out that a proper choice for the estimated value function in the Boltzmann strategy can significantly increase the likelihood of convergence to the optimal joint action. 2 FMQ Heuristic In difficult coordination problems, such as the climbing game and the penalty game, the way to achieve convergence to the optimal joint action is by influencing the learners towards their individual components of the optimal joint action(s).

In a single-agent learning scenario, Q-learning is guaranteed to converge to the optimal action independent of the action selection strategy. In other words, given the assumption of a stationary reward function, single-agent Q-learning will converge to the optimal policy for the problem. However, in a multi-agent setting, the action selection strategy becomes crucial for convergence to any joint action. A major challenge in defining a suitable strategy for the selection of actions is to strike a balance between exploring the usefulness of moves that have been attempted only a few times and exploiting those in which the agent’s confidence in getting a high reward is relatively strong.

Section 3 contains a brief description of the experimental setup, focused on the advice-exchange algorithm. Section 4 concerns the discussion of the initial results, and finally in section 5, some conclusions and a preview of the future work to be done in this direction. 2 Related Work The work on cooperative learning had some important contributions in the early nineties with the results published by Whitehead [3], Lin [4] and Tan [1]. All these works focused on cooperation of Learning Agents that use variations of Q-Learning [5].

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Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning by Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)


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