Symbolic dynamic programming for first-order POMDPs

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2010, Pages 1140-1146

  Sanner, Scott ^a   Kersting, Kristian ^b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b FRAUNHOFER IAIS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEHAVIORAL RESEARCH; DECISION MAKING; MARKOV PROCESSES;

DYNAMIC PROGRAMMING ALGORITHM; EXPRESSIVE POWER; OBSERVATION SPACE; OPTIMAL DYNAMICS; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; PROGRAMMING SOLUTIONS; RELATIONAL REPRESENTATIONS; SEQUENTIAL DECISION MAKING;

DYNAMIC PROGRAMMING;

EID: 77958579615     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Boutilier, C; Reiter, R.; and Price, B. 200I. Symbolic dynamic programming for first-order MDPs. In IJCAI, 690-697.
2. Doshi, F., and Roy, N. 2008. The permutable POMDP: fast solutions to POMDPs for preference elicitation. In AAMAS, 493-500.
3. Enderton, H. A. 1972. A Mathematical Introduction to Logic. New York: Academic Press.
4. Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence. (I - 2): 99-134.
5. Kim, K.-E. 2008. Exploiting symmetries in POMDPs for point-based algorithms. AAAI, 1043-1048.
6. Puterman, M. L. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: Wiley.
7. Reiter, R. 200I. Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press.
8. Sanner, S., and Boutilier, C. 2007. Approximate solution techniques for factored first-order MDPs. In (ICAPS-07), 288-295.
9. Sanner, S., and Boutilier, C. 2009. Practical solution techniques for first-order MDPs. Artificial Intelligence. 173:748-488.
10. Sanner, S. 2008. First-order Decision-theoretic Planning in Structured Relational Environments. Ph.D. Dissertation, University of Toronto, Toronto, ON, Canada.
11. Shani, G.; Brafman, R. I.; Shimony, S. E.; and Poupart, P. 2008. Efficient add operations for point-based algorithms. In ICAPS.
12. Sondik, E. J. 197I. The Optimal Control of Partially Observable Markov Processes. Ph.D. Dissertation, Stanford University.
13. Spaan, M. T. J., and Vlassis, N. 2005. Perseus: Randomized point-based value iteration for POMDPs. JAIR 24:195-220.
14. Vassos, S.; Lakemeyer, G.; and Levesque, H. 2008. First-order strong progression for local-effect basic action theories. In KR.
15. Wang, C, and Khardon, R. 2010. Relational partially observable MDPs. In AAAI.
16. Wang, C, and Schmolze, J. 2005. Planning with POMDPs using a compact, logic-based representation. In IEEE ICTAI, 523-530.
17. Wang, C. 2007. First Order Markov Decision Processes. Ph.D. Dissertation, Tufts University, USA.