Learning Agent Models in SeSAm: (Demonstration)
2013 (English)In: / [ed] Ito, Jonker, Gini and Shehory, The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2013Conference paper (Refereed)
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.
Research subject Computer and Systems Science
IdentifiersURN: urn:nbn:se:oru:diva-29234OAI: oai:DiVA.org:oru-29234DiVA: diva2:623924
12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), May 2013, St. Paul, USA