We present a novel case-based plan recognition method that interprets observations of plan behavior using an incrementally constructed case library of past observations. The technique is novel in several ways. It combines plan recognition with case-based reasoning and leverages the strengths of both. The representation of a plan is a sequence of action-state pairs rather than only the actions. The technique compensates for the additional complexity with a unique abstraction scheme augmented by pseudo-isomorphic similarity relations to represent indices into the case base. Past cases are used to predict subsequent actions by adapting old actions and their arguments. Moreover, the technique makes predictions despite observations of unknown actions. This paper evaluates the algorithms and their implementation both analytically and empirically. The evaluation criteria include prediction accuracy at both an abstract and a concrete level and across multiple domains with and without case-adaptation. In each domain the system starts with an empty case base that grows to include thousands of past observations. Results demonstrate that this new method is accurate, robust, scalable, and general across domains.

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Our research investigates a case-based approach to plan recognition using incomplete incrementallylearned plan libraries. To learn plan libraries, one must be able to process novel input. Retrieval based on similarities among concrete planning situations rather than among planning actions enables recognition despite the occurrence of newly observed planning actions and states. In addition we explore the benefits of predictions using a measure that we call abstract similarity. Abstract similarity is used when a concrete state maps to no known abstract state. Instead a search is performed for nearby abstract states based on a nearest neighbor technique. Such a retrieval scheme enables accurate prediction in light of extremely novel observed situations. The properties of retrieval in abstract state-spaces are investigated in three standard planning domains. We first determine optimal radii to use that determines a spherical sub-hyperspace that limits the search. Then experimental results show that significant improvements in the recognition process is obtained using abstract similarity.