Learning policy constraints through dialogue

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.
Original languageEnglish
Title of host publicationProceedings of the AAAI Fall Symposium on The Uses of Computational Argumentation
EditorsTrevor Bench-Capon, Simon Parsons, Henry Prakken
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages6
Publication statusPublished - Nov 2009

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Emele, C. D., Norman, T. J., Guerin, F., & Parsons, S. (2009). Learning policy constraints through dialogue. In T. Bench-Capon, S. Parsons, & H. Prakken (Eds.), Proceedings of the AAAI Fall Symposium on The Uses of Computational Argumentation Association for the Advancement of Artificial Intelligence.