Learning policies through argumentation-derived evidence (extended abstract)

Chukwuemeka D Emele, Timothy J Norman, Frank Guerin, Simon Parsons

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Abstract

We present an efficient approach for identifying, learning and modeling the policies of others during collaborative activities. In a set of experiments, we demonstrate that more accurate models of others' policies (or norms) can be developed more rapidly using various forms of evidence from argumentation-based dialogue.
Original languageEnglish
Title of host publicationProceedings of the 9th Internatinal Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010)
EditorsWiebe van der Hoek, Gal Kaminka, Michael Luck, Sandip Sen
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Number of pages2
ISBN (Electronic)0982657100, 9780982657119
Publication statusPublished - 5 Oct 2010
EventNinth International Joint Conference on Autonomous Agents and Multiagent Systems - Toronto, Canada
Duration: 10 May 2010 → …

Conference

ConferenceNinth International Joint Conference on Autonomous Agents and Multiagent Systems
CountryCanada
CityToronto
Period10/05/10 → …

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Cite this

Emele, C. D., Norman, T. J., Guerin, F., & Parsons, S. (2010). Learning policies through argumentation-derived evidence (extended abstract). In W. van der Hoek, G. Kaminka, M. Luck, & S. Sen (Eds.), Proceedings of the 9th Internatinal Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) International Foundation for Autonomous Agents and Multiagent Systems.