Abductive Markov Logic for Plan Recognition

Authors

  • Parag Singla University of Texas at Austin
  • Raymond Mooney University of Texas at Austin

Abstract

Plan recognition is a form of abductive reasoning that involves inferring plans that best explain sets of observed actions. Most existing approaches to plan recognition and other abductive tasks employ either purely logical methods that donot handle uncertainty, or purely probabilistic methods thatdo not handle structured representations. To overcome these limitations, this paper introduces an approach to abductive reasoning using a first-order probabilistic logic, specifically Markov Logic Networks (MLNs). It introduces several novel techniques for making MLNs efficient and effective for abduction. Experiments on three plan recognition datasets showthe benefit of our approach over existing methods.

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Published

2011-08-04

How to Cite

Singla, P., & Mooney, R. (2011). Abductive Markov Logic for Plan Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1069-1075. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/8018