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Learning Agent Models in SeSAm: (Demonstration)
Örebro University, School of Science and Technology. (AASS)
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-1470-6288
2013 (English)In: / [ed] Ito, Jonker, Gini and Shehory, The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2013Conference paper, Published paper (Refereed)
Abstract [en]

Designing the agent model in a multiagent simulation is a challenging task due to the generative nature of such systems. In this contribution we present an extension to the multiagent simulation platform SeSAm, introducing a learning-based design strategy for building agent behavior models.

Place, publisher, year, edition, pages
The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2013.
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-29234OAI: oai:DiVA.org:oru-29234DiVA, id: diva2:623924
Conference
12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), May 2013, St. Paul, USA
Available from: 2013-05-29 Created: 2013-05-29 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

AAMAS2013_MABLE_Demo(445 kB)269 downloads
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Type fulltextMimetype application/pdf

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AAMAS2013/p1373.pdf

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
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Output format
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