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.
|Title of host publication||Proceedings of the AAAI Fall Symposium on The Uses of Computational Argumentation|
|Editors||Trevor Bench-Capon, Simon Parsons, Henry Prakken|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||6|
|Publication status||Published - Nov 2009|
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. http://www.staff.science.uu.nl/~prakk101/uses/emele.pdf