Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation

  • Ryosuke KojimaEmail author
  • Taisuke Sato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9046)


We propose new methods for goal and plan recognition based on prefix and infix probability computation in a probabilistic context-free grammar (PCFG) which are both applicable to incomplete data. We define goal recognition as a task of identifying a goal from an action sequence and plan recognition as that of discovering a plan for the goal consisting of goal-subgoal structure respectively. To achieve these tasks, in particular from incomplete data such as sentences in a PCFG that often occurs in applications, we introduce prefix and infix probability computation via parse trees in PCFGs and compute the most likely goal and plan from incomplete data by considering them as prefixes and infixes.

We applied our approach to web session logs taken from the Internet Traffic Archive whose goal and plan recognition is important to improve websites. We tackled the problem of goal recognition from incomplete logs and empirically demonstrated the superiority of our approach compared to other approaches which do not use parsing. We also showed that it is possible to estimate the most likely plans from incomplete logs. All prefix and infix probability computation together with the computation of the most likely goal and plan in this paper is carried out using logic-based modeling language PRISM.


Prefix probability PCFG Plan recognition Session log 


  1. 1.
    Arlitt, M.F., Williamson, C.L.: Web server workload characterization: the search for invariants. ACM SIGMETRICS Perform. Eval. Rev. 24, 126–137 (1996)CrossRefGoogle Scholar
  2. 2.
    Chi, Z.: Statistical properties of probabilistic context-free grammars. Comput. Linguist. 25(1), 131–160 (1999)MathSciNetGoogle Scholar
  3. 3.
    De Raedt, L., Kimmig, A., Toivonen, H.: Problog: a probabilistic prolog and its application in link discovery. IJCAI 7, 2462–2467 (2007)Google Scholar
  4. 4.
    Goodman, B.A., Litman, D.J.: On the interaction between plan recognition and intelligent interfaces. User Model. User-Adapt. Interact. 2(1–2), 83–115 (1992)CrossRefGoogle Scholar
  5. 5.
    Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256–265. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  6. 6.
    Huang, L.: Advanced dynamic programming in semiring and hypergraph frameworks. In: COLING (2008)Google Scholar
  7. 7.
    The Internet Traffic Archive (2001).
  8. 8.
    Jelinek, F., Lafferty, J.D.: Computation of the probability of initial substring generation by stochastic context-free grammars. Comput. Linguist. 17(3), 315–323 (1991)Google Scholar
  9. 9.
    Lesh, N., Rich, C., Sidner, C.L.: Using plan recognition in human-computer collaboration. In: Kay, J. (ed.) UM99 User Modeling. CISM International Centre for Mechanical Sciences - Courses and Lectures, pp. 23–32. Springer, Vienna (1999)CrossRefGoogle Scholar
  10. 10.
    Nederhof, M.J., Satta, G.: Computation of infix probabilities for probabilistic context-free grammars. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1213–1221. Association for Computational Linguistics (2011)Google Scholar
  11. 11.
    Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of Internationall Conference on Logic Programming, ILCP 1995 (1995)Google Scholar
  12. 12.
    Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. J. Artif. Intell. Res. 15, 391–454 (2001)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Sato, T., Kameya, Y.: New advances in logic-based probabilistic modeling by PRISM. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 118–155. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  14. 14.
    Sato, T., Meyer, P.: Infinite probability computation by cyclic explanation graphs. Theor. Pract. Logic Program. 15, 1–29 (2013)zbMATHGoogle Scholar
  15. 15.
    Stolcke, A.: An efficient probabilistic context-free parsing algorithm that computes prefix probabilities. Comput. Linguist. 21(2), 165–201 (1995)MathSciNetGoogle Scholar
  16. 16.
    Wetherell, C.S.: Probabilistic languages: a review and some open questions. ACM Comput. Surv. (CSUR) 12(4), 361–379 (1980)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of Information Science and EngineeringTokyo Institute of TechnologyMeguro-ku, TokyoJapan
  2. 2.AI research centerAISTKoto-ku, TokyoJapan

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