Collaborative decision making among agents in a team is a complex activity, and tasks to achieve individual objectives may conflict in a team context. A number of argumentation-based models have been proposed to address the problem, the rationale being that the revelation of background information and constraints can aid in the discovery and resolution of conflicts. To date, however, no empirical studies have been conducted to substantiate these claims. In this paper, we discuss a model, grounded on argumentation schemes, that captures potential conflicts due to scheduling and causality constraints, and individual goals and norms. We evaluate this model in complex collaborative planning problems and show that such a model facilitates the sharing of relevant information pertaining to plan, goal and normative conflicts. Further, we show that this focussed information sharing leads to more effective conflict resolution, particularly in the most challenging problems.
|Title of host publication||ECAI 2012|
|Subtitle of host publication||20th European Conference on Artificial Intelligence|
|Editors||L. De Raedt, C. Bessiere, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, P. Lucas|
|Number of pages||6|
|Publication status||Published - Aug 2012|
|Name||Frontiers in Artificial Intelligence and Applications|
- Multi-agent systems