Bounded branching and modalities in non-deterministic planning

ICAPS 2006 - Proceedings, Sixteenth International Conference on Automated Planning and Scheduling

Volumn 2006, Issue , 2006, Pages 42-51

  Bonet, Blai ^a  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COMPUTER PROGRAMMING LANGUAGES; DECISION MAKING; INDUSTRIAL MANAGEMENT; PROBLEM SOLVING;

CONFORMANT PLANNING; MODAL FORMULAE; NO BRANCH PLANS; NON DETERMINISTIC PLANNING;

STRATEGIC PLANNING;

EID: 33746055310     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Bertsekas, D. 1995. Dynamic Programming and Optimal Control, (2 Vols). Athena Scientific.
2. Bonet, B., and Geffner, H. 2000. Planning with incomplete information as heuristic search in belief space. In Chien, S.; Kambhampati, S.; and Knoblock, C., eds., Proc. 6th International Conf. on Artificial Intelligence Planning and Scheduling, 52-61. Breckenridge, CO: AAAI Press.
3. Bonet, B., and Geffner, H. 2001. Planning as heuristic search. Artificial Intelligence 129(1-2):5-33.
4. Bonet, B., and Geffner, H. 2003. Faster heuristic search algorithms for planning with uncertainty and full feedback. In Gottlob, G., ed., Proc. 18th International Joint Conf. on Artificial Intelligence, 1233-1238. Acapulco, Mexico: Morgan Kaufmann.
5. Bylander, T. 1994. The computational complexity of propositional STRIPS planning. Artificial Intelligence 69:165-204.
6. Cassandra, A.; Littman, M.; and Kaelbling, L. 1994. Acting optimally in partially observable stochastic domains. In Hayes-Roth, B., and Korf, R., eds., Proc. 12th National Conf. on Artificial Intelligence, 1023-1028. Seattle, WA: AAAI Press / MIT Press.
7. Chandra, A.; Kozen, D.; and Stockmeyer, L. 1981. Alternation. Journal of the ACM 28(1): 114-133.
8. Chvatal, V. 1983. Mastermind. Combinatorica 3(3-4):325-329.
9. Du, D., and Ko, K. 2000. Theory of Computational Complexity. New York, NY: Wiley-Interscience.
10. Fikes, R., and Nilsson, N. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 1: 27-120.
11. Giacomo, G. D., and Vardi, M. Y. 1999. Automatatheoretic approach to planning for temporally extended goals. In Biundo, S., and Fox, M., eds., Proc. 5th European Conf. on Planning, 226-238. Durham, UK: Springer: LNCS 1809.
12. Goldman, R. P., and Boddy, M. S. 1996. Expressive planning and explicit knowledge. In Drabble, B., ed., Proc. 3rd International Conf. on Artificial Intelligence Planning Systems. Edinburgh, Scotland: AAAI Press.
13. Hansen, E., and Zilberstein, S. 2001. LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence 129:35-62.
14. Haslum, P., and Jonsson, P. 1999. Some results on the complexity of planning with incomplete information. In Biundo, S., and Fox, M., eds., Proc. 5th European Conf. on Planning, 308-318. Durham, UK: Springer: LNCS 1809.
15. Hoffmann, J., and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14:253-302.
16. Hopcroft, J., and Ullman, J. 1979. Introduction to Automata Theory, Languages, and Computation. Addison-Wesley.
17. Kaelbling, L.; Littman, M.; and Cassandra, A. 1999. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101:99-134.
18. Littman, M. 1997. Probabilistic propositional planning: representations and complexity. In Kuipers, B., and Webber, B., eds., Proc. 14th National Conf. on Artificial Intelligence. 748-754. Providence, RI: AAAI Press / MIT Press.
19. Majercik, S., and Littman, M. 1998. Maxplan: A new approach to probabilistic planning. In Simmons, R.; Veloso, M.; and Smith, S., eds., Proc. 4th International Conf. on Artificial Intelligence Planning Systems, 86-93. Pittsburgh, PA: AAAI Press.
20. McDermott, D. 1996. A heuristic estimator for means ends analysis in planning. In Drabble, B., ed., Proc. 3rd International Conf. on Artificial Intelligence Planning Systems, 150-157. Edinburgh, Scotland: AAAI Press.
21. Meuleau, N., and Smith, D. 2003. Optimal limited contingency planning. In Meek, C., and Kjaerulff, U., eds., Proc. 19th Conf. on Uncertainty in Artificial Intelligence. Acapulco, Mexico: Morgan Kaufmann.
22. Papadimitriou, C. 1993. Computational Complexity. Addison-Wesley.
23. Pednault, E. 1989. ADL: Exploring the middle ground between Strips and the situation calculus. In Brachman, R.; Levesque, H.; and Reiter, R., eds., Proc. 1st International Conf. on Principles of Knowledge Representation and Reasoning, 324-332. Toronto, Canada: Morgan Kaufmann.
24. Puterman, M. 1994. Markov Decision Processes - Discrete Stochastic Dynamic Programming. John Wiley and Sons, Inc.
25. Rintanen, J. 2004a. Complexity of planning with partial observability. In Zilberstein, S.; Koenig, S.; and Koehler, J., eds., Proc. 14th International Conf. on Automated Planning and Scheduling, 345-354. Whistler, Canada: AAAI Press.
26. Rintanen, J. 2004b. Distance estimates for planning in the discrete belief space. In McGuinness, D. L., and Ferguson, G., eds., Proc. 19th National Conf. on Artificial Intelligence, 525-530. San Jose, California: AAAI Press / MIT Press.
27. Smith, D., and Weld, D. 1998. Conformant graphplan. In Mostow, J., and Rich, C., eds., Proc. 15th National Conf. on Artificial Intelligence, 889-896. Madison, WI: AAAI Press / MIT Press.
28. Sondik, E. 1978. The optimal control of partially observable Markov decision processes over the infinite horizon: discounted costs. Operations Research 26(2).
29. Turner, H. 2002. Polynomial-length planning spans the polynomial hierarchy. In Flesca, S., and Giovambattista, I., eds., Proc. 8th European Conf. on Logics in Artificial Intelligence, 111-124. Cosenza, Italy: Springer: LNCS 2424.