Abstract
Plan recognition consists of building an interpretation of an observed behavior in terms of the plans and goals that can be attributed to an agent. It can be thus considered as a form of understanding. Plan recognition is often compared with planning—they are considered as opposite processes according to two criteria: the mechanism employed to reach the solution—selection or construction — and the knowledge involved in both forms of plan reasoning. On these grounds, plan recognition has often been considered as an ill-defined “understanding task”, different from problem solving. Also, while knowledge-level models of planning can be found in the literature, the main modeling approaches have made no attempt to rationalize plan recognition. In this paper, plan recognition is analyzed as problem solving behavior, and an interpretation model for this task is proposed. The situation considered is one of keyhole recognition from low-level data in a dynamic environment, with the aim of providing decision support in a critical domain.
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Cañamero, D. (1994). Modeling plan recognition for decision support. In: Steels, L., Schreiber, G., Van de Velde, W. (eds) A Future for Knowledge Acquisition. EKAW 1994. Lecture Notes in Computer Science, vol 867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58487-0_9
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DOI: https://doi.org/10.1007/3-540-58487-0_9
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