In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction.

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In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 82--87, Atlanta, GA, July 2010.

@inproceedings{raghavan:star-ai10, title={Bayesian Abductive Logic Programs}, author={Sindhu Raghavan and Raymond Mooney}, booktitle={Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10)}, month={July}, address={Atlanta, GA}, pages={82--87}, url="http://www.cs.utexas.edu/users/ai-lab?raghavan:star-ai10", year={2010} }

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