Recognizing the agent’s goals incrementally: planning graph as a basis

Abstract

Plan recognition, the inverse problem of plan synthesis, is important wherever a system is expected to produce a kind of cooperative or competitive behavior. Most plan recognizers, however, suffer the problem of acquisition and hand-coding a larger plan library. This paper is aims to show that modern planning techniques can help build plan recognition systems without suffering such problems. Specifically, we show that the planning graph, which is an important component of the classical planning system Graphplan, can be used as an implicit, dynamic planning library to represent actions, plans and goals. We also show that modern plan generating technology can be used to find valid plans in this framework. In this sense, this method can be regarded as a bridge that connects these two research fields. Empirical and theoretical results also show that the method is efficient and scalable.