Planning with continuous resources in stochastic domains

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2005, Pages 1244-1251

  Mausam, ^a   Benazera, Emmanuel ^b,d   Brafman, Ronen ^b,e   Meuleau, Nicolas ^b   Hansen, Eric A ^c  

^a University of Washington   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

^d Research Institute for Advanced Computer Science   (United States)

^e QSS GROUP INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED PLANNING; COMPUTATIONAL EFFORT; PLANETARY EXPLORATION ROVERS; REACHABILITY CONSTRAINTS; RESOURCE CONSTRAINT; RESOURCE LIMITATIONS; SEARCH ALGORITHMS; STOCHASTIC DOMAINS;

ARTIFICIAL INTELLIGENCE; INTERPLANETARY SPACECRAFT; STOCHASTIC SYSTEMS;

ALGORITHMS;

EID: 84880762931     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. E. Altman. Constrained Markov Decision Processes. Chapman and HALL/CRC, 1999.
2. J. Bresina, R. Dearden, N. Meuleau, S. Ramakrishnan, D. Smith, and R. Washington. Planning under continuous time and resource uncertainty: A challenge for AI. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pages 77-84, 2002.
3. Z. Feng, R. Dearden, N. Meuleau, and R. Washington. Dynamic programming for structured continuous Markov decision problems. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, pages 154-161, 2004.
4. J.H. Friedman, J.L. Bentley, and R.A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Trans. Mathematical Software, 3(3):209-226, 1977.
5. C. Guestrin, M. Hauskrecht, and B. Kveton. Solving factored MDPs with continuous and discrete variables. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, pages 235-242, 2004.
6. E. Hansen and S. Zilberstein. LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence, 129:35-62, 2001. (Pubitemid 32498415)
7. N. Meuleau, R. Dearden, and R. Washington. Scaling up decision theoretic planning to planetary rover problems. In AAAI-04: Proceedings of the Workshop on Learning and Planning in Markov Processes Advances and Challenges, pages 66-71, Technical Report WS-04-08, AAAI Press, Menlo Park, CA, 2004.
8. J. Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, 1984.
9. L. Pedersen, D. Smith, M. Deans, R. Sargent, C. Kunz, D. Lees, and S.Rajagopalan. Mission planning and target tracking for autonomous instrument placement. In Submitted to 2005 IEEE Aerospace Conference, 2005.
10. D. Smith. Choosing objectives in over-subscription planning. In Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling, pages 393-401, 2004.
11. M. van den Briel, M.B. Do R. Sanchez and, and S. Kambhampati. Effective approaches for partial satisfation (over-subscription) planning. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, pages 562-569, 2004.
12. H.L.S. Younes and R.G. Simmons. Solving generalized semi-Markov decision processes using continuous phase-type distributions. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, pages 742-747, 2004.