Planning with Incomplete Information as Heuristic Search in Belief Space

Proceedings of the 5th International Conference on Artificial Intelligence Planning Systems, AIPS 2000

Volumn , Issue , 2000, Pages 52-61

  Bonet, Blai ^a   Geffner, Héctor ^a  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

Author keywords

[No Author keywords available]

Indexed keywords

HEURISTIC ALGORITHMS; MODULAR ROBOTS;

BELIEF SPACE; CLASSICAL PLANNING; HEURISTIC SEARCH; PLANNING WITH INCOMPLETE INFORMATION; PROBABILISTICS; PROBABILITY: DISTRIBUTIONS; PROBLEM REPRESENTATION; SEARCH ALGORITHMS;

PROBABILITY DISTRIBUTIONS;

EID: 85166261608     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. Astrom, K. 1965. Optimal control of markov decision processes with incomplete state estimation. J. Math. Anni. Appi. 10:174 205.
2. Barto, A.: Bradtke. S.: and Singh, S. 1995. Learning to act using real-time dynamic programming. Artificial Intelligence 72:81 138.
3. Beek, P. V., and Chen. X. 1999. CPIan: a constraint programming approach to planning. In ft™'. AAA 1-99.
4. Bertsekas, D., and Tsitsiklis, .1. 1996. Neuro-Dynamic Programming. Athena. Scientific.
5. Bertsekas, D. 1995. Dynamic Programming and Optimal Control, Vols I and II. Athena Scientific.
6. Blum, A., and Fürst. M. 1995. Fast planning through planning graph analysis. In Proceedings of IJCAI-95.
7. Bonct, B.. and Geffner. H. 1998a. High-level planning and control with incomplete information using POMDPs. In Proceedings AAAI Fall Symp. on Cognitive Robotics. Bonet, B.. and Ceffner. H. 1998b. Solving large POMDPs using real time dynamic programming. In Proc. AAAI Fall Symp. on POMl) PH.
8. Bonet, B., and Geffner. H. 1999. Planning as heuristic search: New results. In Proceedings of FA'P-99. Springer.
9. Bonet, B.: Loerincs, G.: and Geffner. H. 1997. A robust and fast action selection mechanism for planning. In Proceedings of AAA l-97, HA 719. MIT Press.
10. Chrisman, L. 1992. Reinforcement learning with perceptual aliasing. In Proceedings AAAI-92.
11. Cimatti. A., and Roveri, M. 1999. Conformant planning via model checking. In Proceedings of ECP-99. Springer
12. Fagin, R.; TIalpern, J.: Moses, Y.: and Vardi. M. 1995. Reasoning about Knowledge. MIT Press.
13. Fikes, R., and Nilsson, N. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 1:27- 120.
14. Geffner, H., and Wainer, J. 1998. Modeling action, knowledge and control. In Proceedings ECAI-9H. Wiley.
15. Geffner, H. 1999. Functional strips: a more general language for planning and problem solving. Logic-based Al Workshop. Washington D.C
16. Genesereth, M., and Nourbakhsh, I. 199.1. Time-Saving tips for problem solving with incomplete information. In Proceedings of AAAI-93.
17. Giunchiglia, F., and Traverso, P. 1999. Planning as model checking. In Proceedings of ECP-99. Springer. Hansen, E., and Zilberstein, S. 1998. Heuristic search in cyclic AND/OR graphs. In Proc. AAAI-98, 412-418.
18. Hauskrecht M. 1997. Planning and Control in Stochastic Domains with Incomplete Information. Ph.D. Dissertation, MIT.
19. Kaebling, I,.; Littman, M.; and Cassandra. T. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101(1 2):99-134.
20. Kautz. H.. and Selman, B. 1996. Pushing the envelope: Planning, propositional logic, and stochastic search. In Proceedings of AAAl-90, 1194 1201.
21. Kautz. H., and Selman, B. 1999. Unifying S AT-based and Graph-based planning. In Proceedings MCA 1-99.
22. Knuth, D. 1971. The Art of Computer Programming. Vol. Ill: Sorting and Si arching. Addison-Wesley.
23. Korf. R. 1990. Real-time heuristic search. Artificial Intelligence 42:189 -211.
24. Kushmcrick, N.: Hanks, S.; and Weld, D. 1995. An algorithm for probabilistic planning. Artificial Intelligence 76:2.19-286.
25. Levesqiie, H. 1996. What is planning in the presence of sensing. In Proceedings AAAi-96. 1119-1146. Portland, Oregon: MIT Press.
26. Lovejoy, W. 1991. Computationally feasible bounds for partially observed markov decision processes. Operations Research 162 175.
27. Majercik, S., and Littman, M. 1998. Maxplan: A new approach to probabilistic planning. In Proc. A1PS-9M. 8693.
28. McDermott, D. 1996. A heuristic estimator for means-ends analysis in planning. In Proc. Third Int. Conf. on AI Planning'Systems (A IPS-96).
29. McDermott. D. 1998. PDDL the planning domain definition language. Available at http://ftp.es.yale.edu/pub/mcdermott.
30. Nilsson, N. 1980. Principles of Artificial Intelligence. Tioga.
31. Parr. R., and Russell, S. 1995. Approximating optimal policies for partially observable stochastic domains. In Proceedings IJCAI-9H.
32. Pearl, J. 1981. Heuristics. Morgan Kaufmann.
33. Peot. M. 1998. Decision-Theoretic Planning. Ph.D. Dissertation, Dept. Engineering-Economic Systems. Stanford University.
34. Piiterman. M. 1994. Markör Decision Processes; Discrete Dynamic Stochastic Programming. John Wiley.
35. Refanidis, I., and Vlahavas, I. 1999. GRT: A domain independent, heuristic for Strips worlds based on greedy regression tables. In Proceedings of ECP-99. Springer.
36. Rintanen, J. 1999. Constructing conditional plans by a theorem prover. J. of Al Research 10:123-352.
37. Smith. D., and Weld, D. 1998. Conformant graphplan. In Prociedings AAAI-9H, 889-896. AAAI Press.
38. Sondik, E. 1971. The Optimal Control of Partially Observable Markov Proctsscs. Ph.D. Dissertation, Stanford University.
39. Vossen. T.: Ball. M.; Lotem. A.: and Xau, D. 1999. On the use of integer programming models in ai planning. In Proceedings 1.1 CA 1-99.
40. Weld. D.; Anderson, C; and Smith, D. 1998. Extending Graphplan to handle uncertainty and sensing actions. In Proc. AAA1-9X, 897- 904. AAA! Press.