Mao, W., Gratch, J.
Proceedings of the AAMAS 2004 Workshop on Agent Tracking: Modeling Other Agents from Observations
(NY City, July, 2004)
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Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by incrementally using plan knowledge and observations to change state probabilities. We also discuss the work and compare it with other probabilistic models in the paper.
