Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso,'s Adaptive Agents and Multi-Agent Systems: Adaptation and 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 structures is an rising and intriguing interdisciplinary sector of analysis and improvement concerning synthetic intelligence, machine technology, software program engineering, and developmental biology, in addition to cognitive and social science.

This publication surveys the cutting-edge during this rising box via drawing jointly completely chosen reviewed papers from comparable workshops; in addition to papers by means of top researchers particularly solicited for this ebook. The articles are equipped 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 resources for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning

Example text

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.

Agent i: receives the best average quality (bqj) from all other agents (j  i). Current quality for Agent i is cqi. 2. Loop: Agent i: gets state s for evaluation. 3. Agent i: calculates k = arg maxj(bqj), for all agents (j  i). 4. Agent i: if cqi < d bqk, where 0

Best average quality measured during one epoch) at the beginning of each epoch. Cooperative Learning Using Advice Exchange 41 Table 1. Steps of the advice-exchange sequence for an advisee agent (i) and an advisor agent (k). 1. Agent i: receives the best average quality (bqj) from all other agents (j  i). Current quality for Agent i is cqi. 2. Loop: Agent i: gets state s for evaluation. 3. Agent i: calculates k = arg maxj(bqj), for all agents (j  i). 4. Agent i: if cqi < d bqk, where 0

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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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