By Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)
Adaptive brokers and Multi-Agent structures is an rising and intriguing interdisciplinary region of analysis and improvement related to synthetic intelligence, computing device 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 by means of drawing jointly completely chosen reviewed papers from comparable workshops; in addition to papers by way of best researchers in particular solicited for this publication. 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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Extra resources for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
However, we have found a variant of the climbing game in which both heuristics perform poorly: the fully stochastic climbing game. This game has the characteristic that all joint actions are probabilistically linked with two rewards. The average of the two rewards for each action is the same as the original reward from the deterministic version of the climbing game so the two games are functionally equivalent. For the rest of this discussion, we assume a 50% probability. The reward function for the stochastic climbing game is included in Table 8.
The penalty game table. Agent 1 a b c a 10 0 k Agent 2 b 0 2 0 c k 0 10 game, it is clear that the penalty game is a challenging testbed for the study of learning coordination in multi-agent systems. 3 A Q-Learning Approach to Learning of Coordination A popular technique for learning coordination in cooperative single-stage games is onestep Q-learning, a reinforcement learning technique. In this section, we first introduce the general approach, followed by a discussion of the novel FMQ heuristic for action selection.
Incorporating this reward into the learning process can be so detrimental that both agents tend to avoid playing the same action again. In contrast, when choosing action c, miscoordination is not punished so severely. Therefore, in most cases, both agents are easily tempted by action c. The reason is as follows: if agent 1 plays c, then agent 2 can play either b or c to get a positive reward (6 and 5 respectively). Even if agent 2 plays a, the result is not catastrophic since the reward is 0. Similarly, if agent 2 plays c, whatever agent 1 plays, the resulting reward will be at least 0.
Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning by Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)