A decision-theoretic model of assistance

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 1879-1884

  Fern, Alan ^a   Natarajan, Sriraam ^a   Judah, Kshitij ^a   Tadepalli, Prasad ^a  

^a Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFORT; COMPUTER ENVIRONMENTS; DECISION-THEORETIC; GOAL-DIRECTED; HUMAN SUBJECTS; INTELLIGENT ASSISTANTS; KNOWLEDGE WORKERS; OVERALL COSTS;

ARTIFICIAL INTELLIGENCE;

COMPUTER GAMES;

EID: 80052984053     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. J. L. Ambite, G. Barish, C. A. Knoblock, M. Muslea, J. Oh, and S. Minton. Getting from here to there: Interactive planning and agent execution for optimizing travel. In IAAI, pages 862-869, 2002.
2. D. P. Bertsekas and J. N. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, 1996.
3. N. Blaylock and J. F. Allen. Statistical goal parameter recognition. In ICAPS, 2004.
4. J. Boger, P. Poupart, J. Hoey, C. Boutilier, G. Fernie, and A. Mihailidis. A decision-theoretic approach to task assistance for persons with dementia. In IJCAI, 2005.
5. C. Boutilier, T. Dean, and S. Hanks. Decision-theoretic planning: Structural assumptions and computational leverage. JAIR, 11:1-94, 1999. (Pubitemid 129628760)
6. H. Bui, S. Venkatesh, and G. West. Policy recognition in the abstract hidden markov models. JAIR, 17, 2002.
7. CALO. Cognitive agent that learns and organizes, http://calo.sri.com., 2003.
8. W. W. Cohen, V. R. Carvalho, and T. M. Mitchell. Learning to classify email into speech acts. In Proceedings of Empirical Methods in NLP, 2004.
9. P. Doshi. A particle filtering algorithm for interactive pomdps, 2004.
10. C. Guestrin, D. Koller, R. Parr, and S. Venkataraman. Efficient solution algorithms for factored MDPs. JAIR, pages 399-468, 2003.
11. E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In In Proc UAI, pages 256-265, Madison, WI, July 1998.
12. B. Hui and C. Boutilier. Who's asking for help?: a bayesian approach to intelligent assistance. In IUI, pages 186-193, 2006.
13. M. J. Kearns, Y. Mansour, and A. Y. Ng. A sparse sampling algorithm for near-optimal planning in large markov decision processes. In IJCAI, 1999.
14. T. M. Mitchell, R. Caruana, D. Freitag, J.McDermott, and D. Zabowski. Experience with a learning personal assistant. Communications of the ACM, 37(7):80-91, 1994.
15. A.W. Moore and C. G. Atkeson. Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning, 13:103-130, 1993.
16. D. V. Pynadath and M. P.Wellman. Probabilistic state-dependent grammars for plan recognition. In UAI, pages 507-514, 2000.
17. P. Varakantham, R. T. Maheswaran, and M. Tambe. Exploiting belief bounds: practical pomdps for personal assistant agents. In AAMAS, 2005.