Exploration of the robustness of plans

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 834-839

  Fox, Maria ^a   Howey, Richard ^a   Long, Derek ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATA THEORY; MATHEMATICAL MODELS; MONTE CARLO METHODS; PLANNING; PROBLEM SOLVING; RANDOM PROCESSES;

DOMAIN MODEL; EXECUTION-TIME VARIATIONS; STOCHASTIC ROBUSTNESS TESTING;

ROBUSTNESS (CONTROL SYSTEMS);

EID: 33750742248     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Cichy, B.; Chien, S.; Schaffer, S.; Tran, D.; Rabideau, G.; and Sherwood, R. 2004. Validating the Autonomous EO-1 Science Agent. In International Workshop on Planning and Scheduling for Space (IWPSS).
2. Cochran, W. 1977. Sampling Techniques. New York: Wiley.
3. Davenport, A.; Gefflot, C.; and Beck, J. 2001. Slack-based techniques for robust schedules. In Proceedings of the 6th European Conference on Planning (ECP).
4. Fox, M., and Long, D. 2003. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of AI Research 20.
5. Ginsberg, M.; Parkes, A.; and Roy, A. 1998. Supermodels and Robustness. In Proc. of AAAI-98.
6. Grosu, R., and Smolka, S. 2005. Monte Carlo model checking. In Proc. 11th International Conference on Tools for the Construction and Analysis of Systems (TACAS), 271-286. LNCS 3440: Springer-Verlag.
7. Gupta, V.; Henzinger, T.; and Jagadeesan, R. 1997. Robust Timed Automata. In HART'97: Hybrid and Real-time Systems, LNCS 1201, 331-345. Springer-Verlag.
8. Hoffmann, J., and Edelkamp, S. 2005. The Deterministic Part of IPC-4: An Overview. Journal of AI Research (JAIR) 24:519-579.
9. Little, I.; Aberdeen, D.; and Thiebaux, S. 2005. Prottle: A Probabilistic Temporal Planner. In Proc. of AAAI-05.
10. Lockwood, R., and Hayes, D. 2000. Sample size for biological studies. In Schneider, J., ed., Manual of Fisheries Survey Methods II. Michigan Department of Natural Resources, Lansing. chapter 6.
11. Mahtab, T.; Sullivan, G.; and Williams, B. C. 2004. Automated Verification of Model-based Programs under Uncertainty. In Proceedings of the 4th International Conference on Intelligent Systems Design and Application.
12. Morris, P., and Muscettola, N. 2005. Temporal Dynamic Controllability Revisited. In Proc. of AAAI.
13. Morris, P.; Muscettola, N.; and Vidal, T. 2001. Dynamic Control of Plans with Temporal Uncertainty. In Proceedings of IJCAI, 494-502.
14. Muscettola, N. 1994. HSTS: Integrating planning and scheduling. In Zweben, M., and Fox, M., eds., Intelligent Scheduling. San Mateo, CA: Morgan Kaufmann. 169-212.
15. Tsamardinos, I., and Pollack, M. 2003. Efficient solution techniques for disjunctive temporal reasoning problems. Artificial Intelligence Journal 151(1-2):43-89.
16. Vidal, T., and Ghallab, M. 1996. Constraint-based temporal management in planning: the IxTeT approach. In Proc. of 12th European Conference on AI.
17. Younes, H., and Simmons, R. 2002. Probabilistic Verification of Discrete Event Systems using Acceptance Sampling. In Proc. 14th International Conference on Computer Aided Verification, 223-235.