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)


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.


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.


  1. 1.
    Blaylock, N., Allen, J.: Generating artificial corpora for plan recognition. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 179–188. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Blaylock, N., Allen, J.: Recognizing instantiated goals using statistical methods. In: Kaminka, G. (ed.) Workshop on MOO 2005, pp. 79–86 (2005)Google Scholar
  3. 3.
    Blaylock, N., Allen, J.F.: Statistical goal parameter recognition. In: ICAPS 2004, pp. 297–305 (2004)Google Scholar
  4. 4.
    Blythe, J., Hobbs, J., Domingos, J., Kate, P., Mooney, R., Implementing, R.: weighted abduction in Markov logic. In: IWCS 2011 (January 2011)Google Scholar
  5. 5.
    Bui, H.H.: A general model for online probabilistic plan recognition. In: IJCAI 2003 (2003)Google Scholar
  6. 6.
    Charniak, E., Goldman, R.: A probabilistic model of plan recognition. In: AAAI 1991, pp. 160–165 (1991)Google Scholar
  7. 7.
    Charniak, E., Goldman, R.P.: A semantics for probabilistic quantifier-free first-order languages, with particular application to story understanding. In: IJCAI 1989, Detroit, MI (1989)Google Scholar
  8. 8.
    Charniak, E., McDermott, D.: Introduction to Artificial Intelligence. Addison, Reading (1985)zbMATHGoogle Scholar
  9. 9.
    Chen, J., Muggleton, S., Santos, J.: Learning probabilistic logic models from probabilistic examples. Machine Learning 73(1), 55–85 (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Elvira-Consortium: Elvira: An environment for probabilistic graphical models. In: Proceedings of the Workshop on Probabilistic Graphical Models, Cuenca, Spain (2002)Google Scholar
  11. 11.
    Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT, Cambridge (2007)zbMATHGoogle Scholar
  12. 12.
    Gogate, V., Dechter, R.: Samplesearch: A scheme that searches for consistent samples. In: AISTATS 2007 (2007)Google Scholar
  13. 13.
    Huynh, T.N., Mooney, R.J.: Online max-margin weight learning with Markov logic networks. In: SDM 2011 (2011)Google Scholar
  14. 14.
    Kate, R.J., Mooney, R.J.: Probabilistic abduction using Markov logic networks. In: IJCAI 2009 Workshop on Plan, Activity, and Intent Recognition, Pasadena, CA (July 2009)Google Scholar
  15. 15.
    Kautz, H.A., Allen, J.F.: Generalized plan recognition. In: AAAI, Philadelphia, PA, pp. 32–37 (1986)Google Scholar
  16. 16.
    Kersting, K., De Raedt, L.: Towards combining inductive logic programming with bayesian networks. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 118–131. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Kersting, K., De Raedt, L.: Bayesian Logic Programming: Theory and Tool. In: Getoor, L., Taskar, B. (eds.) An Introduction to Statistical Relational Learning. MIT, Cambridge (2007)Google Scholar
  18. 18.
    Kersting, K., Raedt, L.D.: Basic principles of learning Bayesian logic programs. In: Probabilistic Inductive Logic Programming, pp. 189–221 (2008)Google Scholar
  19. 19.
    Kimmig, A., De Raedt, L., Toivonen, H.: Probabilistic explanation based learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 176–187. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  21. 21.
    Muggleton, S.H.: Learning structure and parameters of stochastic logic programs. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 198–206. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Nau, D., Ilghami, O., Kuter, U., Murdock, J.W., Wu, D., Yaman, F.: Shop2: An HTN planning system. Journal of Artificial Intelligence Research 20, 379–404 (2003)zbMATHGoogle Scholar
  23. 23.
    Ng, H.T., Mooney, R.J.: The role of coherence in abductive explanation. In: AAAI 1990, Detroit, MI, pp. 337–442 (July 1990)Google Scholar
  24. 24.
    Ng, H.T., Mooney, R.J.: Abductive plan recognition and diagnosis: A comprehensive empirical evaluation. In: KR 1992, Cambridge, MA, pp. 499–508 (October 1992)Google Scholar
  25. 25.
    Nilsson, D.: An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Statistics and Computing 8, 159–173 (1998)CrossRefGoogle Scholar
  26. 26.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, MKP, CA (1988)Google Scholar
  27. 27.
    Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64, 81–129 (1993)CrossRefzbMATHGoogle Scholar
  28. 28.
    Pople, H.E.: On the mechanization of abductive logic. In: IJCAI 1973, pp. 147–152 (1973)Google Scholar
  29. 29.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)CrossRefGoogle Scholar
  30. 30.
    Rosner, B.: Fundamentals of Biostatistics. Duxbury Press (2005)Google Scholar
  31. 31.
    Sato, T.: A statistical learning method for logic programs with distribution semantics. In: ICLP 1995, pp. 715–729. MIT Press, Cambridge (1995)Google Scholar
  32. 32.
    Stickel, M.E.: A Prolog-like inference system for computing minimum-cost abductive explanations in natural-language interpretation. Tech. Rep. Tech. Note 451, SRI International, CA (September 1988)Google Scholar

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

Personalised recommendations