Plan recognition addresses the problem of inferring an agents goals from its action. Applications range from anticipating care-takers' needs to predicting volatile situations. In this contribution, we describe a prototype plan recognition system that is based on the well-researched theory of (weighted) finite tree automata. To illustrate the system?s capabilities, we use data gathered from matches in the real-time strategy game StarCraft II. Finally, we discuss how more advanced plan operators can be accommodated for in this framework while retaining computational efficiency by taking after the field of formal model checking and over-approximating the target language.
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