Abductive Plan Recognition by Extending Bayesian Logic Programs

  • Sindhu Raghavan
  • Raymond J. Mooney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)

Abstract

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 cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, we develop an approach to plan recognition using Bayesian Logic Programs (BLPs), which combine first-order logic and Bayesian networks. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). We learn the parameters in BALPs using the Expectation Maximization algorithm adapted for BLPs. Finally, we present an experimental evaluation of BALPs on three benchmark data sets and compare its performance with the state-of-the-art for plan recognition.

Keywords

Bayesian Network Inductive Logic Programming Horn Clause Proof Tree Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sindhu Raghavan
    • 1
  • Raymond J. Mooney
    • 1
  1. 1.Department of Computer ScienceUniversity of TexasAustinUSA

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