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dc.contributor.authorJunges, Robert
dc.contributor.authorKlügl, Franziska
dc.date2013-08-01
dc.date.accessioned2018-01-08T09:16:41Z
dc.date.available2018-01-08T09:16:41Z
dc.date.issued2013
dc.identifier.issn1610-1987
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/11361
dc.description.abstractIn this project report, we describe ongoing research on supporting the development of agent-based simulation models. The vision is that the agents themselves should learn their (individual) behavior model, instead of letting a human modeler test which of the many possible agent-level behaviors leads to the correct macro-level observations. To that aim, we integrate a suite of agent learning tools into SeSAm, a fully visual platform for agent-based simulation models. This integration is the focus of this contribution.
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 27, No. 3
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectAgent learning
dc.subjectAgent modeling
dc.subjectAgent-based simulation
dc.titleLearning Tools for Agent-Based Modeling and Simulation
dc.typeText/Journal Article
mci.reference.pages273-280
gi.identifier.doi10.1007/s13218-013-0258-z


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