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Probabilistic Abduction



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In Proceedings of the IJCAI 09 Workshop on Plan Activity and Intent Recognition PAIR 09 Pasadena CA July 2009 Probabilistic Abduction using Markov Logic Networks Rohit J Kate Raymond J Mooney Department of Computer Sciences The University of Texas at Austin 1 University Station C0500 Austin TX 78712 0233 USA rjkate mooney cs utexas edu Abstract Abduction is inference to the best explanation of a given set of evidence It is important for plan or intent recognition systems Traditional approaches to abductive reasoning have either used first order logic which is unable to reason under uncertainty or Bayesian networks which can handle uncertainty using probabilities but cannot directly handle an unbounded number of related entities This paper proposes a new method for probabilistic abductive reasoning that combines the capabilities of first order logic and graphical models by using Markov logic networks Experimental results on a plan recognition task demonstrate the effectiveness of this method 1 Introduction Abduction is inference to the best explanation Its applications include tasks in which observations need to be explained by inferring the best hypothesis for example plan or intent recognition medical diagnosis fault diagnosis etc Most previous approaches to automated abduction have been based either on first order logic and determine a small set of assumptions sufficient to deduce the observations to be explained or on Bayesian networks and compute the posterior probability of alternative explanations given the observations The former approaches cannot handle uncertainty in the evidence or background knowledge and are incapable of estimating the likelihood of alternative explanations While the latter approaches handle uncertainty they do not directly handle structured representations involving relations amongst multiple entities since Bayesian networks are essentially propositional in nature In this paper we introduce a new method that combines the strengths of



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