Plan recognition is the task of predicting an agent's top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring plans that best explain observed actions. Most existing approaches to plan recognition and other abductive reasoning tasks either use first-order logic (or subsets of it) or probabilistic graphical models. While the former cannot handle uncertainty in the data, the latter cannot handle structured representations. To overcome these limitations, we explore the application of statistical relational models that combine the strengths of both first-order logic and probabilistic graphical models to plan recognition. Specifically, we introduce two new approaches to abductive plan recognition using Bayesian Logic Programs (BLPs) and Markov Logic Networks (MLNs). Neither of these formalisms is suited for abductive reasoning because of the deductive nature of the underlying logical inference. In this work, we propose approaches to adapt both these formalisms for abductive plan recognition. We present an extensive evaluation of our approaches on three benchmark datasets on plan recognition, comparing them with existing state-of-the-art methods.

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In Plan, Activity, and Intent Recognition: Theory and Practice, Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P. (Eds.), pp. 57--85, Burlington, MA 2014. Morgan Kaufmann.

@incollection{raghavan:bookchapter14, title={Plan Recognition Using Statistical Relational Models}, author={Sindhu Raghavan and Parag Singla and Raymond J. Mooney}, booktitle={Plan, Activity, and Intent Recognition: Theory and Practice}, editor={Sukthankar, G. and Geib and C. and Bui and H.H. and Pynadath and D. and Goldman and R.P.}, address={Burlington, MA}, publisher={Morgan Kaufmann}, pages={57--85}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127424", year={2014} }