Goal recognition with variable-order Markov models

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1635-1640

  Armentano, Marcelo G ^a   Amandi, Analía ^b  

^a Campus Universitario ^*  (Argentina)

^b Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; MARKOV PROCESSES;

EARLY PREDICTION; INTENTION MODELING; INTERFACE AGENT; SOFTWARE APPLICATIONS; USER ACTION; USER GOALS; VARIABLE ORDER MARKOV MODELS;

SOFTWARE AGENTS;

EID: 78751686390     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Armentano, M. G. Recognition of User Intentions with Variable-Order Markov Models. PhD thesis, Universidad Nacional del Centro de la Provincia de Buenos Aires. Argentina. 2008.
2. Bauer, M. From interaction data to plan libraries: A clustering approach. In IJCAI '99: Proceedings of the 6th International Joint Conference on Artificial Intelligence, pages 962-967, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc. 1999.
3. Blaylock, N. and Allen, J. Recognizing instantiated goals using statistical methods. In Gal Kaminka, editor, IJCAI Workshop on Modeling Others from Observations (MOO-2005), pages 79-86, Edinburgh. 2005.
4. Bui, H. H. A general model for online probabilistic plan recognition. In Gottlob, G. and Walsh, T., editors, IJCAI 03, Proceedings of the 8th International Joint Conference on Artificial Intelligence, pages 1309-1318, Acapulco, Mexico. Morgan Kaufmann. 2003.
5. Charniak, E. and Goldman, R. P. (1993). A bayesian model of plan recognition. Artificial Intelligence, 64(1):53-79. 1993.
6. Davison, B. D. and Hirsh, H. Predicting sequences of user actions. In Predicting the Future: AI Approaches to Time Series. AAAI Press. 1998.
7. Duong, T. V., Phung, D. Q., Bui, H. H., and Venkatesh, S. Human behavior recognition with generic exponential family duration modeling in the hidden semi-markov model. In International Conference on Pattern Recognition, volume 3, pages 202-207. 2006.
8. Garland;, A. and Lesh, N. Learning hierarchical task models by demonstration. Technical report, Mitsubishi Electric Research Laboratories. 2002.
9. Goldman, R., Geib, C., and Miller, C. Learning hierarchical task models by defining and redefining examples. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 245-254, San Francisco, CA. Morgan Kaufmann. 1999.
10. Gorniak, P. and Poole, D. Building a stochastic dynamic model of application. In Boutilier, C. and Goldszmidt, M., editors, 6th Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 230-237, Stanford University, Stanford, California, USA. Morgan Kaufmann. 2000.
11. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K. The Lumière project: Bayesian user modeling for inferring the goals and needs of software users. In Cooper, G. F. and Moral, S., editors, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 256-265, San Mateo. Morgan Kaufmann. 1998.
12. Huber, M. and Simpson, R. Recognizing the plans of screen reader users. In Modeling Other Agents from Observations (MOO2004). Workshop W3 at the International Joint Conference on Autonomous Agents and Multi-Agent Systems, Columbia University, NY, USA. 2004.
13. Hunter, J. S. The exponentially weighted moving average. Journal of Quality Technology, 18(4):203-209. 1986.
14. Kautz, H. A formal theory of plan recognition and its implementation. In Allen, J. F., Kautz, H. A., Pelavin, R., and Tenenberg, J., editors, Reasoning About Plans, pages 69-125. Morgan Kaufmann Publishers, San Mateo (CA), USA. 1991.
15. Lesh, N. (1998). Scalable and Adaptive Goal Recognition. PhD thesis, University of Washington. 1998.
16. Liao, L., Patterson, D. J., Fox, D., and Kautz, H. A. Learning and inferring transportation routines. Artificial Intelligence, 171 (5-6) pages 311-331. 2007.
17. Maes, P. Agents that reduce work and information overload. Communications of the ACM. 1994.
18. Nguyen, N. T., Phung, D. Q., Venkatesh, S., and Bui, H. H. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In IEEE Computer Vision and Pattern Recognition or CVPR, pages 955-960. IEEE Computer Society. 2005.
19. Oliver, N., Horvitz, E., and Garg, A. Layered representations for human activity recognition. In Proceedings of the 4th IEEE International Conference on Multimodal Interfaces (ICMI 2002), pages 3-8. IEEE Computer Society. 2002.
20. Philipose, M., Fishkin, K. P., Perkowitz, M., Patterson, D. J., Fox, D., Kautz, H., and Hahnel, D. Inferring activities from interactions with objects. Pervasive Computing Magazine, 3(4) pages 10-17. 2004.
21. Rich, C., Sidner, C. L., and Lesh, N. (2001). COLLAGEN: Applying collaborative discourse theory to human-computer interaction. AI Magazine, 22(4) pages 15-26. 2001.
22. Rissanen, J. "A Universal Data Compression System". IEEE Transactions on Information Theory 29 (5): 656-664. 1983.
23. Ron, D., Singer, Y., and Tishby, N. The power of amnesia: Learning probabilistic automata with variable memory length. Machine Learning, 25(2-3):117-149. 1996.
24. Stumpf, S., Bao, X., Dragunov, A., Dietterich, T. G., Herlocker, J., Johnsrude, K., Li, L., and Shen, J. Predicting user tasks: I know what you're doing! In 20th National Conference on Artificial Intelligence (AAAI-05), Workshop on Human Comprehensible Machine Learning. 2005.
25. Whitworth, B. Polite computing. Behaviour and Information Technology, 24(5):353-363. 2005.