Scaling up decision theoretic planning to planetary rover problems

AAAI Workshop - Technical Report

Volumn WS-04-08, Issue , 2004, Pages 66-71

  Meuleau, Nicolas ^a,b   Dearden, Richard ^a,c   Washington, Rich ^a,c  

^a NASA AMES RESEARCH CENTER   (United States)

^b QSS GROUP INC   (United States)

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

Author keywords

[No Author keywords available]

Indexed keywords

COMMUNICATION LIMITS; DECISION-THEORETIC TECHNIQUES; NASA APPLICATIONS; PLANETARY ROVERS;

AUTONOMOUS AGENTS; CONCURRENCY CONTROL; INTERPLANETARY SPACECRAFT; UNCERTAIN SYSTEMS;

DECISION THEORY;

EID: 32144456417     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Blum, A., and Furst, M. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90:281-300.
2. Blythe, J. 1999. Decision-theoretic planning. AI Magazine 20(2):37-54.
3. Boutilier, C.; Dearden, R.; and Goldszmidt, M. 2000. Stochastic dynamic programming with factored representations. Artificial Intelligence 121:49-107.
4. Boutillier, C.; Brafman, R.; and Geib, C. 1998. Structured reachability analysis for Markov decision processes. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. 24-32.
5. Boutillier, C.; Dean, T.; and Hanks, S. 1999. Decision theoretic planning: structural assumptions and computational leverage. Journal of AI Research 11:1-94.
6. Boyan, J., and Littman, M. 2000. Exact solutions to time-dependent MDPs. In Advances in Neural Information Processing Systems 13. 1-7.
7. Bresina, J.; Dearden, R.; Meuleau, N.; Ramakrishnan, S.; Smith, D.; and Washington, R. 2002. Planning under continuous time and resource uncertainty: A challenge for AI. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence.
8. Cassandra, A.; Littman, M.; and Zhang, N. 1997. Incremental Pruning: A simple, fast, exact method for partially observable Markov decision processes. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, 54-61.
9. Dean, T., and Givan, R. 1997. Model minimization in markov decision processes. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, 106-111.
10. Dearden, R.; Meuleau, N.; Ramakrishnan, S.; Smith, D.; and Washington, R. 2003. Incremental contingency planning. In ICAPS'03: Proceedings of the Workshop on Planning under Uncertainty and Incomplete Information, 415-428.
11. Drummond, M.; Bresina, J.; and Swanson, K. 1994. JustIn-Case scheduling. In Proceedings of the Twelfth National Conference on Artificial Intelligence, 1098-1104.
12. Estlin, T.; Fisher, F.; Gaines, D.; Chouinard, C.; Schaffer, S.; and Nesnasg, I. 2002. Continuous planning and execution for an autonomous rover. In Proceedings of the Third International NASA Workshop on Planning and Scheduling for Space.
13. Feng, Z.; Dearden, R.; Meuleau, N.; and Washington, R. 2004. Dynamic programming for structured continuous Markov decision problems. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence. To appear.
14. Friedman, J.; Bentley, J.; and Finkel, R. 1977. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3):209-226.
15. Hoey, J.; St-Aubin, R.; Hu, A.; and Boutilier, C. 1999. SPUDD: Stochastic planning using decision diagrams. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence.
16. Hyafil, N., and Bacchus, F 2003. Conformant probabilistic planning via CSPs. In Proceedings of the Thirteenth International Conference on Automated Planning and Scheduling, 205-214.
17. Jonsson, A.; Morris, P.; Muscettola, N.; Rajan, K.; B.; and Smith. 2000. Planning in interplanetary space: theory and practice. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling, 117-186.
18. Kaelbling, L.; Littman, M.; and Cassandra, A. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101:99-134.
19. Meuleau, N., and Smith, D. 2003. Optimal limited contingency planning. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 417-426.
20. Muscettola, N.; Nayak, P.; Pell, B.; and Williams, B. 1998. Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence 103(1-2):5-17.
21. Pedersen, L.; Bualat, M.; Lees, D.; Smith, D.; and Washington, R. 2003. Integrated demonstration of instrument placement, robust execution and contingent planning. In Proc. of the 7th Int. Symp. on Artificial Intelligence, Robotics and Automation in Space.
22. Smith, D., and Weld, D. 1998. Conformant graphplan. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 889-896.