MGPT: A probabilistic planner based on heuristic search

Journal of Artificial Intelligence Research

Volumn 24, Issue , 2005, Pages 933-944

  Bonet, Blai ^a   Geffner, Héctor ^b  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

^b UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPETITION; DECISION MAKING; FUNCTIONS; HEURISTIC METHODS; MARKOV PROCESSES; PLANNING;

HEURISTIC SEARCH; LOWER BOUNDS; MARKOV DECISION PROCESSES; VALUE FUNCTIONS;

PROBABILISTIC LOGICS;

EID: 31144438031     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1688     Document Type: Article

References (18)

1. Barto, A., Bradtke, S., & Singh, S. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 72, 81-138.
2. Bertsekas, D. (1995). Dynamic Programming and Optimal Control, (2 Vols). Athena Scientific.
3. Blum, A., & Furst, M. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90, 281-300.
4. Bonet, B., & Geffner, H. (2000). Planning with incomplete information as heuristic search in belief space. In Chien, S., Kambhampati, S., & Knoblock, C. (Eds.), Proc. 6th International Conf. on Artificial Intelligence Planning and Scheduling, pp. 52-61, Breckenridge, CO. AAAI Press.
5. Bonet, B., & Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129(1-2), 5-33.
6. Bonet, B., & Geffner, H. (2003a). Faster heuristic search algorithms for planning with uncertainty and full feedback. In Gottlob, G. (Ed.), Proc. 18th International Joint Conf. on Artificial Intelligence, pp. 1233-1238, Acapulco, Mexico. Morgan Kaufmann.
7. Bonet, B., & Geffner, H. (2003b). Labeled RTDP: Improving the convergence of real-time dynamic programming. In Giunchiglia, E., Muscettola, N., & Nau, D. (Eds.), Proc. 13th International Conf. on Automated Planning and Scheduling, pp. 12-21, Trento, Italy. AAAI Press.
8. Bonet, B., Loerincs, G., & Geffner, H. (1997). A robust and fast action selection mechanism for planning. In Kuipers, B., & Webber, B. (Eds.), Proc. 14th National Conf. on Artificial Intelligence, pp. 714-719, Providence, RI. AAAI Press / MIT Press.
9. Cormen, T., Leiserson, C., & Rivest, R. (1990). Introduction to Algorithms. MIT Press.
10. Edelkamp, S. (2001). Planning with pattern databases. In Cesta, A. (Ed.), Proc. 6th European Conf. on Planning, pp. 13-24, Toledo, Spain. Springer: LNCS.
11. Gazen, B., & Knoblock, C. (1997). Combining the expressiveness of UCPOP with the efficiency of Graphplan. In Steel, S., & Alami, R. (Eds.), Proc. 4th European Conf. on Planning, pp. 221-233, Toulouse, France. Springer: LNCS.
12. Haslum, P., Bonet, B., & Geffner, H. (2005). New admissible heuristics for domainindependent planning. In Veloso, M., & Kambhampati, S. (Eds.), Proc. 20 National Conf. on Artificial Intelligence, pp. 1163-1168, Pittsburgh, PA. AAAI Press / MIT Press.
13. Haslum, P., & Geffner, H. (2000). Admissible heuristic for optimal planning. In Chien, S., Kambhampati, S., & Knoblock, C. (Eds.), Proc. 6th International Conf. on Artificial Intelligence Planning and Scheduling, pp. 140-149, Breckenridge, CO. AAAI Press.
14. Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14, 253-302.
15. Korf, R. (1990). Real-time heuristic search. Artificial Intelligence, 42(2-3), 189-211.
16. Nguyen, X., & Kambhampati, S. (2000). Extracting effective and admissible state-space heuristics from the planning graph. In Kautz, H., & Porter, B. (Eds.), Proc. 17th National Conf. on Artificial Intelligence, pp. 798-805, Austin, TX. AAAI Press / MIT Press.
17. Pearl, J. (1983). Heuristics. Morgan Kaufmann.
18. Tarjan, R. E. (1972). Depth first search and linear graph algorithms. SIAM Journal on Computing, 1(2), 146-160.