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A Planning Approach to the Recognition of Multiple Goals

Plan recognition is a ubiquitous task in artificial intelligence and pervasive computing research. The multigoal recognition problem presents a major challenge in the real world of plan recognition. Users often pursue several goals in a concurrent and interleaving manner, where the pursuit of goals may spread over different parts of an activity sequence and may be pursued in parallel. Existing approaches for multigoal problems are probabilistic approaches. They all assume the existence of plan libraries, which require a lot of human efforts in predicting and formalizing plans and may be impractical in many cases. In this paper, we present a novel logic‐based approach to solve the multigoal recognition problem efficiently, without the need of plan libraries, using a state‐of‐the‐art heuristic search planner LAMA. In particular, we first propose the formulation of a multigoal recognition problem based on automated planning. Then we present a bilevel probabilistic plan recognition approach that deals with both concurrent and interleaving goals from observed activity sequences. Experimental results over several domains show that our method has great flexibility and scalability.