Solving generalized semi-Markov decision processes using continuous phase-type distributions

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2004, Pages 742-747

  Younes, Håkan L S ^a   Simmons, Reid G ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; DECISION MAKING; PROBLEM SOLVING; QUALITY ASSURANCE; STOCHASTIC CONTROL SYSTEMS;

GENERALIZED SEMI-MARKOV DECISION PROCESS (GSMDP); QUALITY POLICIES; SEMI-MARKOV DECISION PROCESSES (SMDP); THEORETIC PLANNING;

MARKOV PROCESSES;

EID: 9444297041     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Erlang, A. K. 1917. Solution of some problems in the theory of probabilities of significance in automatic telephone exchanges. The Post Office Electrical Engineers' Journal 10:189-197.
2. Fujita, M.; McGeer, P. C.; and Yang, J. C.-Y. 1997. Multiterminal binary decision diagrams: An efficient data structure for matrix representation. Formal Methods in System Design 10(2/3): 149-169.
3. Glynn, P. W. 1989. A GSMP formalism for discrete event systems. Proceedings of the IEEE 77(1): 14-23.
4. Guestrin, C.; Koller, D.; and Parr, R. 2002. Multiagent planning with factored MDPs. In Advances in Neural In formation Processing Systems 14: Proc. 2001 Conference. Cambridge, MA: The MIT Press. 1523-1530.
5. Hoey, J.; St-Aubin, R.; Hu, A.; and Boutilier, C. 1999. SPUDD: Stochastic planning using decision diagrams. In Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence, 279-288. Morgan Kaufmann Publishers.
6. Howard, R. A. 1960. Dynamic Programming and Markov Processes. New York, NY: John Wiley & Sons.
7. Howard, R. A. 1971. Dynamic Probabilistic Systems, volume II. New York, NY: John Wiley & Sons.
8. Jensen, A. 1953. Markoff chains as an aid in the study of Markoff processes. Skandinavisk Aktuarietidskrift 36:87-91.
9. k/1/N queues. In Proc. Performance '80, 117-125. ACM Press.
10. Matthes, K. 1962. Zur Theorie der Bedienungsprozesse. In Trans. Third Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, 513-528. Publishing House of the Czechoslovak Academy of Sciences.
11. Neuts, M. F. 1981. Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach. Baltimore, MD: Johns Hopkins University Press.
12. Osogami, T., and Harchol-Balter, M. 2003. A closed-form solution for mapping general distributions to minimal PH distributions. In Proc. 13th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, volume 2794 of LNCS, 200-217. Springer.
13. Puterman, M. L. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY: John Wiley & Sons.
14. Rintanen, J. 2003. Expressive equivalence of formalism for planning with sensing. In Proc. Thirteenth International Conference on Automated Planning and Scheduling, 185-194. AAAI Press.
15. Sauer, C. H., and Chandy, K. M. 1975. Approximate analysis of central server models. IBM Journal of Research and Development 19(3):301-313.