Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners

Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010

Volumn , Issue , 2010, Pages 1121-1126

  Ramírez, Miquel ^a   Geffner, Hector ^a,b  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLETE INFORMATION; CONDITION; COST DIFFERENCES; DETERMINISTICS; INITIAL STATE; PLAN LIBRARIES; PLAN RECOGNITION; PLANNING ALGORITHMS; PROBABILISTICS; PROBABILITY: DISTRIBUTIONS;

PROBABILITY DISTRIBUTIONS;

EID: 85094392602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Baker, C. L.; Saxe, R.; and Tenenbaum, J. B. 2009. Action understanding as inverse planning. Cognition 113(3):329-349.
2. Bui, H. H.; Phung, D.; Venkatesh, S.; and Phan, H. 2008. The hidden permutation model and location-based activity recognition. In Proc. AAAI-08.
3. Bui, H. 2003. A general model for online probabilistic plan recognition. In Proc. IJCAI-03, 1309-1318.
4. Cohen, P. R.; Perrault, C. R.; and Allen, J. F. 1981. Beyond question answering. In Lehnert, W., and Ringle, M., eds., Strategies for Natural Language Processing. LEA.
5. Doucet, A.; de Freitas, N.; Murphy, K.; and Russell, S. 2000. Rao-Blackwellised particle filtering for dynamic Bayesian networks. In Proc. UAI-2000, 176-183.
6. Gazen, B., and Knoblock, C. 1997. Combining the expressiveness of UCPOP with the efficiency of Graphplan. In Proc. ECP-97, 221-233. Springer.
7. Geib, C. W., and Goldman, R. 2002. Requirements for plan recognition in network security systems. In Proc. Int. Symp. on Recent Advances in Intrusion Detection.
8. Geib, C. W., and Goldman, R. P. 2009. A probabilistic plan recognition algorithm based on plan tree grammars. Artificial Intelligence 173(11):1101-1132.
9. Goldszmidt, M., and Pearl, J. 1996. Qualitative probabilities for default reasoning, belief revision, and causal modeling. Artificial Intelligence 84(1-2):57-112.
10. Haslum, P. 2008. Additive and Reversed Relaxed Reachability Heuristics Revisited. Proceedings of the 2008 Inter-nation Planning Competition.
11. Huber, M. J.; Durfee, E. H.; and Wellman, M. P. 1994. The automated mapping of plans for plan recognition. In Proc. UAI-94, 344-351.
12. Kautz, H., and Allen, J. F. 1986. Generalized plan recognition. In Proc. AAAI-86, 32-37.
13. Kautz, H., and Selman, B. 1999. Unifying SAT-based and Graph-based planning. In Dean, T., ed., Proceedings IJCAI-99, 318-327. Morgan Kaufmann.
14. Lekavý, M., and Návrat, P. 2007. Expressivity of Strips-like and HTN-like planning. In Proc. 1st KES Int. Symp.KES-AMSTA 2007, 121-130.
15. Lesh, N., and Etzioni, O. 1995. A sound and fast goal recognizer. In Proc. IJCAI-95, 1704-1710.
16. Pentney, W.; Popescu, A.; Wang, S.; Kautz, H.; and Philipose, M. 2006. Sensor-based understanding of daily life via large-scale use of common sense. In Proc. AAAI-06.
17. Ramirez, M., and Geffner, H. 2009. Plan recognition as planning. In Proc. 21st Intl. Joint Conf. on Artificial Intelligence, 1778-1783. AAAI Press.
18. Richter, S.; Helmert, M.; and Westphal, M. 2008. Landmarks revisited. In Proc. AAAI, 975-982.
19. Wu, J.; Osuntogun, A.; Choudhury, T.; Philipose, M.; and Rehg, J. 2007. A scalable approach to activity recognition based on object use. In Proc. of ICCV-07.
20. Yang, Q. 2009. Activity Recognition: Linking low-level sensors to high-level intelligence. In Proc. IJCAI-09, 20-26.