Probabilistic planning with clear preferences on missing information

Artificial Intelligence

Volumn 173, Issue 5-6, 2009, Pages 696-721

  Likhachev, Maxim ^a   Stentz, Anthony ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Heuristic search; Partially Observable Markov Decision Processes; Planning; Planning with missing information; Planning with uncertainty

Indexed keywords

ELECTRIC NETWORK ANALYSIS; HEURISTIC METHODS; MODULAR ROBOTS; NAVIGATION; ROBOTICS; STOCHASTIC CONTROL SYSTEMS; TRAFFIC CONGESTION;

DECISION-THEORETIC; EXPERIMENTAL ANALYSIS; HEURISTIC SEARCH; MEMORY REQUIREMENTS; ORDERS OF MAGNITUDES; PARTIAL KNOWLEDGE; PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES; PLANNING PROBLEMS; PROBABILISTIC PLANNING; REAL-WORLD PROBLEMS; ROBOT NAVIGATIONS; SCHEDULING PROBLEMS; SPECIAL PROPERTIES; WEATHER CONDITIONS;

PLANNING;

EID: 60649088051     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2008.10.014     Document Type: Article

References (36)

1. Papadimitriou C.H., and Tsitsiklis J.N. The complexity of Markov Decision Processes. Mathematics of Operations Research 12 3 (1987) 441-450
2. Baral C., Kreinovich V., and Trejo R. Computational complexity of planning and approximate planning in the presence of incompleteness. Artificial Intelligence 122 1-2 (2000) 241-267
3. Barto A., Bradtke S., and Singh S. Learning to act using real-time dynamic programming. Artificial Intelligence 72 (1995) 81-138
4. Hart P.E., Nilsson N.J., and Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems, Science, and Cybernetics SSC-4 2 (1968) 100-107
5. Bonet B., and Geffner H. 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 (2000), AAAI Press, Breckenridge, CO 52-61
6. Puterman M. Markov Decision Processes: Discrete Stochastic Dynamic Programming (1994), John Wiley and Sons
7. M. Likhachev, A. Stentz, PPCP: The proofs, Tech. Rep., University of Pennsylvania Philadelphia, PA, 2008
8. A. Stentz, The focussed D* algorithm for real-time replanning, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1995
9. S. Koenig, M. Likhachev, D* Lite, in: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI), 2002
10. S. Koenig, M. Likhachev, Adaptive A*, in: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2005, poster abstract
11. M. Likhachev, A. Stentz, Goal directed navigation with uncertainty in adversary locations, in: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2007
12. M. Likhachev, A. Stentz, Path clearance using multiple scout robots, in: Proceedings of the Army Science Conference (ASC), 2006
13. Nourbakhsh I., and Genesereth M. Assumptive planning and execution: a simple, working robot architecture. Autonomous Robots Journal 3 1 (1996) 49-67
14. S. Koenig, Y. Smirnov, Sensor-based planning with the freespace assumption, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1996
15. A. Stentz, Map-based strategies for robot navigation in unknown environments, in: AAAI Spring Symposium on Planning with Incomplete Information for Robot Problems, 1996
16. Hansen E., and Zilberstein S. LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence 129 (2001) 35-62
17. B. Bonet, H. Geffner, Faster heuristic search algorithms for planning with uncertainty and full feedback, in: Proceedings of the 18th International Joint Conference on Artificial Intelligence, 2003, pp. 1233-1238
18. Likhachev M., Gordon G., and Thrun S. Planning for Markov Decision Processes with sparse stochasticity. Advances in Neural Information Processing Systems (NIPS) 17 (2004), MIT Press, Cambridge, MA
19. S. Yoon, A. Fern, R. Givan, FF-replan: A baseline for probabilistic planning, in: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2007
20. O. Buffet, D. Aberdeen, The factored policy gradient planner, in: Proceedings of the Fifth International Planning Competition (IPC), 2006
21. Boutilier C., and Poole D. Computing optimal policies for partially observable decision processes using compact representations. Proceedings of the National Conference on Artificial Intelligence (AAAI-96) (1996), AAAI Press/The MIT Press, Portland, OR 1168-1175
22. X. Boyen, D. Koller, Tractable inference for complex stochastic processes, in: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), 1998, pp. 33-42
23. C. Guestrin, D. Koller, R. Parr, Solving factored pomdps with linear value functions, in: Proceedings of the Workshop on Planning under Uncertainty and Incomplete Information, 2001
24. K. Poon, A fast heuristic algorithm for decision-theoretic planning, Ph.D. thesis, The Hong Kong University of Science and Technology, 2001
25. N. Roy, G. Gordon, Exponential family pca for belief compression in POMDPs, in: Advances in Neural Information Processing Systems, 2002
26. J. Pineau, G. Gordon, S. Thrun, Point-based value iteration: An anytime algorithm for POMDPs, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2003
27. M. Spaan, N. Vlassis, A point-based POMDP algorithm for robot planning, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2004, pp. 2399-2404
28. B. Bonet, An ε{lunate}-optimal grid-based algorithm for Partially Observable Markov Decision Processes, in: Proceedings of the International Conference on Machine Learning, 2002
29. Lovejoy W.S. Computationally feasible bounds for partially observed Markov Decision Processes. Operations Research 39 1 (1991) 162-175
30. R. Zhou, E.A. Hansen, An improved grid-based approximation algorithm for POMDPs, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2001
31. T. Smith, R.G. Simmons, Heuristic search value iteration for POMDPs, in: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), 2004
32. Baral, Son, Approximate reasoning about actions in presence of sensing and incomplete information, in: Proceedings of International Logic Programming Symposium (ILPS), 1997
33. R. Washington, BI-POMDP: Bounded, incremental, partially-observable Markov-model planning, in: Proceedings of the European Conference on Planning (ECP), 1997, pp. 440-451
34. F.C.D. Szer, S. Zilberstein, Maa*: A heuristic search algorithm for solving decentralized POMDPs, in: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), 2005
35. G. Shani, R.I. Brafman, S.E. Shimony, Forward search value iteration for POMDPs, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2007
36. D. Ferguson, A. Stentz, S. Thrun, PAO* for planning with hidden state, in: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004