Experiments in learning prototypical situations for variants of the pursuit game
- We present an approach to learning cooperative behavior of agents. Our ap-proach is based on classifying situations with the help of the nearest-neighborrule. In this context, learning amounts to evolving a set of good prototypical sit-uations. With each prototypical situation an action is associated that should beexecuted in that situation. A set of prototypical situation/action pairs togetherwith the nearest-neighbor rule represent the behavior of an agent.We demonstrate the utility of our approach in the light of variants of thewell-known pursuit game. To this end, we present a classification of variantsof the pursuit game, and we report on the results of our approach obtained forvariants regarding several aspects of the classification. A first implementationof our approach that utilizes a genetic algorithm to conduct the search for a setof suitable prototypical situation/action pairs was able to handle many differentvariants.
Verfasser*innenangaben: | Jörg Denzinger, Matthias Fuchs |
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URN: | urn:nbn:de:hbz:386-kluedo-639 |
Dokumentart: | Preprint |
Sprache der Veröffentlichung: | Englisch |
Jahr der Fertigstellung: | 1999 |
Jahr der Erstveröffentlichung: | 1999 |
Veröffentlichende Institution: | Technische Universität Kaiserslautern |
Datum der Publikation (Server): | 03.04.2000 |
Quelle: | In Proceedings on the International Conference on Multi-Agent Systems (ICMAS-1996 |
Fachbereiche / Organisatorische Einheiten: | Kaiserslautern - Fachbereich Informatik |
DDC-Sachgruppen: | 0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Lizenz (Deutsch): | Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |