SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces

Robotics: Science and Systems

Volumn 4, Issue , 2009, Pages 65-72

  Kurniawati, Hanna ^a   Hsu, David ^a   Lee, Wee Sun ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84960123032     PISSN: 23307668     EISSN: 2330765X     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. M. Hauskrecht, "Value-function approximations for partially observable Markov decision processes," J. Artificial Intelligence Research, Vol. 13, pp. 33-94, 2000.
2. J. Hoey, A. von Bertoldi, P. Poupart, and A. Mihailidis, "Assisting persons with dementia during handwashing using a partially observable Markov decision process," in Proc. Int. Conf. on Vision Systems, 2007.
3. K. Hsiao, L. Kaelbling, and T. Lozano-Pérez, "Grasping POMDPs," in Proc. IEEE Int. Conf. on Robotics & Automation, 2007, pp. 4485-4692.
4. D. Hsu, W. Lee, and N. Rong, "What makes some POMDP problems easy to approximate?" in Advances in Neural Information Processing Systems (NIPS), 2007.
5. -, "A point-based POMDP planner for target tracking," in Proc. IEEE Int. Conf. on Robotics & Automation, 2008, pp. 2644-2650.
6. L. Kaelbling, M. Littman, and A. Cassandra, "Planning and acting in partially observable stochastic domains," Artificial Intelligence, Vol. 101, no. 1-2, pp. 99-134, 1998.
7. O. Madani, S. Hanks, and A. Condon, "On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems," in Proc. Nat. Conf. on Artificial Intelligence, 1999, pp. 541-548.
8. A. Makarenko, S. Williams, F. Bourgault, and H. Durrant-Whyte, "An experiment in integrated exploration," in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, 2002.
9. C. Papadimitriou and J. Tsisiklis, "The complexity of Markov decision processes," Mathematics of Operations Research, Vol. 12, no. 3, pp. 441-450, 1987.
10. J. Pineau, G. Gordon, and S. Thrun, "Point-based value iteration: An anytime algorithm for POMDPs," in Proc. Int. Jnt. Conf. on Artificial Intelligence, 2003, pp. 477-484.
11. J. Pineau, M. Montemerlo, M. Pollack, N. Roy, and S. Thrun, "Towards robotic assistants in nursing homes: Challenges and results," Robotics & Autonomous Systems, Vol. 42, no. 3-4, pp. 271-281, 2003.
12. P. Poupart and C. Boutilier, "Value-directed compression of POMDPs," in Advances in Neural Information Processing Systems (NIPS). The MIT Press, 2003, vol. 15, pp. 1547-1554.
13. M. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, 1994.
14. N. Roy, G. Gordon, and S. Thrun, "Finding aproximate POMDP solutions through belief compression," J. Artificial Intelligence Research, Vol. 23, pp. 1-40, 2005.
15. G. Shani, R. Brafman, and S. Shimony, "Adaptation for changing stochastic environments through online POMDP policy learning," in Proc. Eur. Conf. on Machine Learning, 2005, pp. 61-70.
16. -, "Forward search value iteration for POMDPs," in Proc. Int. Jnt. Conf. on Artificial Intelligence, 2007.
17. R. Smallwood and E. Sondik, "The optimal control of partially observable Markov processes over a finite horizon," Operations Research, Vol. 21, pp. 1071-1088, 1973.
18. T. Smith and R. Simmons, "Heuristic search value iteration for POMDPs," in Proc. Uncertainty in Artificial Intelligence, 2004, pp. 520-527.
19. -, "Point-based POMDP algorithms: Improved analysis and implementation," in Proc. Uncertainty in Artificial Intelligence, 2005.
20. M. Spaan and N. Vlassis, "A point-based POMDP algorithm for robot planning," in Proc. IEEE Int. Conf. on Robotics & Automation, 2004.