Incorporating Observer Biases in Keyhole Plan Recognition (Efficiently!)
Plan recognition is the process of inferring other agents' plans and goals based on their observable actions. Essentially all previous work in plan recognition has focused on the recognition process itself, with no regard to the use of the information in the recognizing agent. As a result, low-likelihood recognition hypotheses that may imply significant meaning to the observer, are ignored in existing work. In this paper, we present novel efficient algorithms that allows the observer to incorporate her own biases and preferences---in the form of a utility function---into the plan recognition process. This allows choosing recognition hypotheses based on their expected utility to the observer. We call this Utility-based Plan Recognition (UPR). While reasoning about such expected utilities is intractable in the general case, we present a hybrid symbolic/decision-theoretic plan recognizer, whose complexity is $O(NDT)$, where $N$ is the plan library size, $D$ is the depth of the library and $T$ is the number of observations. We demonstrate the efficacy of this approach with experimental results in several challenging recognition tasks.