Capability Models and Their Applications in Planning
Yu Zhang, Sarath Sreedharan, Subbarao Kambhampati · 2015
One important challenge for a set of agents to achieve more effi-cient collaboration is for these agents to maintain proper models of each other. An important aspect of these models of other agents is that they are often not provided, and hence must be learned from plan execution traces. As a result, these models of other agents are inherently partial and incomplete. Most existing agent mod-els are based on action modeling and do not naturally allow for incompleteness. In this paper, we introduce a new and inherently incomplete modeling approach based on the representation of capa-bilities, which has several unique advantages. First, we show that the structures of capability models can be learned or easily spec-ified, and both model structure and parameter learning are robust to high degrees of incompleteness in plan traces (e.g., with only start and end states partially observed). Furthermore, parameter