AAAI Publications, Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence

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Fixing a Hole in Lexicalized Plan Recognition
Christopher Geib

Last modified: 2011-08-24

Abstract


Previous work has suggested the use of lexicalized grammars for probabilistic plan recognition. Such grammars allow the domain builder to delay commitment to hypothesizing high level goals in order to reduce computational costs. However this delay has limitations. In the case of only partial observation traces, delaying commitment can prevent such algorithms from forming correct conclusions about some goals. This paper presents a heuristic metric to address this limitation. It advocates computing the maximum change in conditional probability across all the computed explanations given the observations explicitly considering a goal of interest.

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