A probabilistic plan recognition algorithm based on plan tree grammars

Artificial Intelligence

Volumn 173, Issue 11, 2009, Pages 1101-1132

  Geib, Christopher W ^a   Goldman, Robert P ^b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b SIFT LLC   (United States)

Author keywords

Action grammars; Bayesian methods; Goal recognition; Intent inference; Plan recognition; Probabilistic grammars; Task tracking

Indexed keywords

ACTION GRAMMARS; BAYESIAN METHODS; GOAL RECOGNITION; INTENT INFERENCE; PLAN RECOGNITION; PROBABILISTIC GRAMMARS; TASK TRACKING;

ALGORITHMS;

BAYESIAN NETWORKS;

EID: 67349165700     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2009.01.003     Document Type: Article

References (55)

1. D. Avrahami-Zilberbrand, G.A. Kaminka, Fast and complete symbolic plan recognition, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2005
2. Bellman R. On a routing problem. Quarterly of Applied Mathematics 16 1 (1958) 87-90
3. Boddy M. Temporal reasoning for planning and scheduling: Lessons learned. In: Tate A. (Ed). Advanced Planning Technology (1996), AAAI Press
4. Boddy M., Carciofini J., and Hadden G. Scheduling with partial orders and a causal model. Proceedings of the Space Applications and Research Workshop (1992), Johnson Space Flight Center
5. M. Boddy, R. Goldman, Empirical results on scheduling and dynamic backtracking, in: Proceedings of the International Symposium on Artificial Intelligence, Robotics, and Automation for Space, 1994
6. Bui H.H., Venkatesh S., and West G. Policy recognition in the abstract hidden Markov model. Journal of Artificial Intelligence Research 17 (2002) 451-499
7. Chan H., and Darwiche A. When do numbers really matter?. Journal of Artificial Intelligence Research 17 (2002) 265-287
8. Charniak E., and McDermott D. Introduction to Artificial Intelligence (1987), Addison-Wesley, Reading MA
9. Charniak E., and Goldman R.P. Plan recognition in stories and in life. In: Henrion M., Schachter R., and Lemmer J. (Eds). Uncertainty in Artificial Intelligence 5 (1990), Elsevier Science Publishing Co., Inc., New York, NY 343-351
10. Charniak E., and Goldman R.P. A Bayesian model of plan recognition. Artificial Intelligence 64 1 (1993) 53-79
11. Charniak E., and Shimony S.E. Cost-based abduction and MAP explanation. Artificial Intelligence 66 2 (1994) 345-374
12. Cohen P.R., Perrault C.R., and Allen J.F. Beyond question answering. In: Lehnert W., and Ringle M. (Eds). Strategies for Natural Language Processing (1981), Lawrence Erlbaum Associates 245-274
13. C. Conati, A.S. Gertner, K. VanLehn, M.J. Druzdzel, On-line student modeling for coached problem solving using Bayesian networks, in: Proceedings of the Sixth International Conference on User Modeling, 1997
14. Cooper G.F. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42 (1990) 393-405
15. Dechter R., Meiri I., and Pearl J. Temporal constraint networks. Artificial Intelligence 49 1 (1991) 61-95
16. K. Erol, J. Hendler, D.S. Nau, UMCP: A sound and complete procedure for hierarchical task network planning, in: Proceedings of the Second International Conference on Artificial Intelligence Planning Systems (AIPS 94), 1994, pp. 249-254
17. Edward Barton J.G. On the complexity of ID/LP parsing. Computational Linguistics 11 4 (1985) 205-218
18. Garey M.R., and Johnson D.S. Computers and Intractability (1979), W.H. Freeman and Company, New York
19. Geib C. Plan recognition. In: Kott A., and McEneaney W. (Eds). Adversarial Reasoning (2006), Chapman and Hall/CRC
20. C.W. Geib, R.P. Goldman, Partial observability in collaborative task tracking, in: Proceedings of the AAAI 2001 Fall Symposium on Collaborative Task Tracking, 2001
21. C.W. Geib, R.P. Goldman, Plan recognition in intrusion detection systems, in: Proceedings of DISCEX II, 2001
22. C.W. Geib, R.P. Goldman, Probabilistic plan recognition for hostile agents, in: Proceedings of the FLAIRS 2001 Conference, 2001
23. C.W. Geib, R.P. Goldman, Recognizing plan/goal abandonment, in: Proceedings of IJCAI 2003, 2003
24. Ghallab M., Nau D., and Traverso P. Automated Planning: Theory and Practice (2004), Morgan Kaufmann Publishers, Inc.
25. R.P. Goldman, C.W. Geib, C.A. Miller, A new model of plan recognition, in: Proceedings of the 1999 Conference on Uncertainty in Artificial Intelligence, 1999
26. Hobbs J.R., Stickel M.E., Appelt D.E., and Martin P.A. Interpretation as abduction. Artificial Intelligence 63 1-2 (1993) 69-142
27. A. Hoogs, A.A. Perera, Video activity recognition in the real world, in: Proceedings of the Conference of the American Association of Artificial Intelligence (2008), 2008, pp. 1551-1554
28. Hopcroft J.E., and Ullman J.D. Introduction to Automata Theory, Languages, and Computation (1979), Addison Wesley
29. I. Horrocks, Using an expressive description logic: FaCT or fiction? in: Proceedings of the 7th International Conference on Principles of Knowledge Representation and Reasoning (KR '98), 1998
30. E. Horvitz, J. Breese, D. Heckerman, D. Hovel, K. Rommelse, The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users, in: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998
31. M.J. Huber, E.H. Durfee, M.P. Wellman, The automated mapping of plans for plan recognition, in: Proceedings of the Twelfth National Conference on Artificial Intelligence 1994, pp. 344-351
32. M. Jan Nederhof, G. Satta, S.M. Shieber, Partially ordered multiset context-free grammars and ID/LP parsing, in: Proceedings of the Eighth International Workshop on Parsing Technologies, Nancy, France, April 2003, pp. 171-182
33. Joshi A., and Schabes Y. Tree-adjoining grammars. Handbook of Formal Languages vol. 3 (1997), Springer Verlag 69-124
34. Kaminka G., Pynadath D., and Tambe M. Monitoring teams by overhearing: A multi-agent plan-recognition approach. Journal of Artificial Intelligence Research 17 1 (2002) 83-135
35. H. Kautz, A formal theory of plan recognition and its implementation, PhD thesis, University of Rochester, 1991
36. H. Kautz, J.F. Allen, Generalized plan recognition, in: Proceedings of the Conference of the American Association of Artificial Intelligence (AAAI-86), 1986, pp. 32-38
37. Ford J.L.R., and Fulkerson D.R. Flows in Networks (1962), Princeton University Press, Princeton
38. L. Liao, D. Fox, H. Kautz, Learning and inferring transportation routines, in: Proceedings of AAAI 2004, 2004
39. Liao L., Fox D., and Kautz H. Extracting places and activities from GPS traces using hierarchical conditional random fields. International Journal of Robotics Research 26 (2007) 119-134
40. L. Liao, D. Fox, H.A. Kautz, Location-based activity recognition using relational Markov networks, in: Proceedings of the 19th International Joint Conference on Artificial Intelligence, 2005, pp. 773-778
41. McCarthy J. Circumscription - A form of non-monotonic reasoning. Artificial Intelligence 13 (1980) 27-39 171-172
42. J. Modayil, T. Bai, H. Kautz, Improving the recognition of interleaved activities, in: Proceedings of the Tenth International Conference on Ubiquitous Computing, 2008
43. Peot M., and Shachter R. Learning from what you don't observe. Uncertainty in Artificial Intelligence, Proceedings of the Fourteenth Conference, San Francisco, CA (1998), Morgan Kaufmann Publishers 439-446
44. Poole D. Logic programming, abduction and probability: A top-down anytime algorithm for estimating prior and posterior probabilities. New Generation Computing 11 3-4 (1993) 377-400
45. Poole D. Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64 (1993) 81-129
46. Pradhan M., Henrion M., Provan G., Favero B.D., and Huang K. The sensitivity of belief networks to imprecise probabilities: An experimental investigation. Artificial Intelligence 85 1-2 (1996) 363-397
47. D. Pynadath, M. Wellman, Probabilistic state-dependent grammars for plan recognition, in: Uncertainty in Artificial Intelligence, Proceedings of the Sixteenth Conference, 2000, pp. 507-514
48. Santos Jr. E. A linear constraint satisfaction approach to cost-based abduction. Artificial Intelligence 65 1 (1994) 1-28
49. Schmidt C., Sridharan N., and Goodson J. The plan recognition problem: An intersection of psychology and artificial intelligence. Artificial Intelligence 11 (1978) 45-83
50. Shimony S.E. Finding MAPs for belief networks Is NP-hard. Artificial Intelligence 68 2 (1994) 399-410
51. Sidner C.L. Plan parsing for intended response recognition in discourse. Computational Intelligence 1 1 (1985) 1-10
52. D.L. Vail, M.M. Veloso, Feature selection for activity recognition in multi-robot domains, in: Proceedings of the Conference of the American Association of Artificial Intelligence (2008), 2008, pp. 1415-1420
53. M. Vilain, Deduction as parsing, in: Proceedings of the Conference of the American Association of Artificial Intelligence (1991), 1991, pp. 464-470
54. Wellman M.P., Breese J.S., and Goldman R.P. From knowledge bases to decision models. Knowledge Engineering Review 7 1 (1992) 35-53
55. Wilensky R. Planning and Understanding (1983), Addison-Wesley