On the benefits of argumentation-derived evidence in learning policies

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

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)
5 Downloads (Pure)


An important and non-trivial factor for effectively developing and resourcing plans in a collaborative context is an understanding of the policy and resource availability constraints under which others operate. We present an efficient approach for identifying, learning and modeling the policies of others during collaborative problem solving activities. The mechanisms presented in this paper will enable agents to build more effective argumentation strategies by keeping track of who might have, and be willing to provide the resources required for the enactment of a plan. We argue that agents can improve their argumentation strategies by building accurate models of others' policies regarding resource use, information provision, etc. In a set of experiments, we demonstrate the utility of this novel combination of techniques through empirical evaluation, in which 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 publicationArgumentation in Multi-Agent Systems - 7th International Workshop, ArgMAS 2010, Revised, Selected and Invited Papers
Number of pages19
Publication statusPublished - 1 Dec 2011
EventSeventh International Workshop on Argumentation in Multi-Agent Systems: ArgMAS 2010 - Toronto, Canada
Duration: 10 May 201010 May 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6614 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


WorkshopSeventh International Workshop on Argumentation in Multi-Agent Systems


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