Experiments in learning prototypical situations for variants of the pursuit game

We present an approach to learning cooperative b.ehavior of agents. Our approach is based on classi~’ing situations with the help of tile nearest-neighbor rule. In this context, learning amounts to evolving a set. of good prototypic’dd situations. With each prototypical situation an action is ,associated that should be executed in that situation. A set of prototypicaa situation/action pairs together with the nearest-neighbor rule represent the behavior of an agent. We demonstrate the utility of our approazh in the light of variants of the well-known pursuit game. To this end, we present a classification of variants of the pursuit game, and wc report on the results of our approach obtained for variants regarding several aspects of the classification. A first implementation of our approach that utilizes a genetic algorithm to conduct the search for a set. of suitable prototypical situation/a~tion pairs was able to handle many different variants.

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