A relational hierarchical model for decision-theoretic assistance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4894 LNAI, Issue , 2008, Pages 175-190

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

^a Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; HIERARCHICAL SYSTEMS; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION;

HIERARCHICAL MODELS; HIERARCHICAL STRUCTURE;

ARTIFICIAL INTELLIGENCE;

EID: 40249086677     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78469-2_19     Document Type: Conference Paper

References (19)

1. Bui, H., Venkatesh, S., West, G.: Policy recognition in the Abstract Hidden Markov Models. In: JAIR, vol. 17 (2002)
2. Boger, J., Poupart, P., Hoey, J., Boutilier, C., Fernie, G., Mihailidis, A.: A Decision-Theoretic Approach to Task Assistance for Persons with Dementia. In: IJCAI 2005, pp. 1293-1299 (2005)
3. Ambite, J.L., Barish, G., Knoblock, C.A., Muslea, M., Oh, J., Minton, S.: Getting from Here to There: Interactive Planning and Agent Execution for Optimizing Travel. In: IAAI, pp. 862-869 (2002)
4. Myers, K., Berry, P., Blythe, J., Conleyn, K., Gervasio, M., McGluinness, D., Morley, D., Pfeffer, A., Pollack, M., Tambe, M.: An Intelligent Personal Assistant for Task and Time Management. AI Magazine (June 2007)
5. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific (1996)
6. Varakantham, P., Maheswaran, R., Tambe, M.: Exploiting belief bounds: Practical POMDPs for personal assistant agents. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) AAMAS 2004. LNCS (LNAI), vol. 3394, Springer, Heidelberg (2005)
7. 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: UAI, pp. 256-265 (1998)
8. Hui, B., Boutilier, C.: Who's asking for help?: a Bayesian approach to intelligent assistance. In: Boutilier, C (ed.) IUI, pp. 186-193 (2006)
9. Fern, A., Natarajan, S., Judah, K., Tadepalli, P.: A Decision-Theoretic Model of Assistance. In: IJCAI (2007)
10. Dietterich, T.G.: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. In: JAIR, vol. 13, pp. 227-303 (2000)
11. Tadepalli, P., Givan, R., Drissens, K.: Relational Reinforcement Learning - An Overview, Workshop on Relational Reinforcement Learning. In: ICML (2004)
12. Jaeger, M.: Relational Bayesian Networks. In: UAI 1997
13. Domingos, P., Richardson, M.: Markov logic networks. Mach. Learn. 62(1-2), 107-136 (2006)
14. Getoor, L., Friedman, N., Koller, D., Pfeffer, A.: Learning Probabilistic Relational Models. In: Dzeroski, S., Lavrac, N. (eds.) Invited contribution to the book Relational Data Mining (2001)
15. Kersting, K., De Raedt, L.: Bayesian Logic Programs. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, Springer, Heidelberg (2000)
16. Murphy, K., Paskin, M.: Linear time inference in hierarchical HMMs. In: NIPS (2001)
17. Fine, S., Singer, Y., Tishby, N.: The Hierarchical Hidden Markov Model: Analysis and Applications. Machine Learning 32(1), 41-62 (1998)
18. Getoor, L., Grant, J.: A Probabilistic Relational Language, Machine Learning Journal, 2005. Machine Learning 62(1-2), 7-33 (2006)
19. Natarajan, S., Tadepalli, P., Altendorf, E., Dietterich, T.G., Fern, A., Restificar, A.: Learning First-Order Probabilistic Models with Combining Rules. In: Proceedings of ICML 2005 (2005)