PUMA: Planning under uncertainty with macro-actions

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2010, Pages 1089-1095

  He, Ruijie ^a   Brunskill, Emma ^b   Roy, Nicholas ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANY-TIME ALGORITHMS; E-OPTIMAL; EXTENDED SEQUENCES; FUTURE OBSERVATIONS; OPEN-LOOP; PLANNING UNDER UNCERTAINTY; STATE OF THE ART;

ARTIFICIAL INTELLIGENCE;

EID: 77958563254     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Cassandra, A.; Kaelbling, L.; and Kurien, J. 1996. Acting under uncertainty: Discrete Bayesian models for mobile robot navigation. In IROS.
2. He, R., and Roy, N. 2009. Efficient POMDP Forward Search by Predicting the Posterior Belief Distribution. Technical report, MIT.
3. Hsiao, K.; Lozano-Perez, T.; and Kaelbling, L. 2008. Robust belief-based execution of manipulation programs. In WAFR.
4. Hsu, D.; Lee, W.; and Rong, N. 2008. A point-based POMDP planner for target tracking. In ICRA.
5. Iba, G. 1989. A heuristic approach to the discovery of macro-operators. Machine Learning 3(4):285-317.
6. Kurniawati, H.; Du, Y.; Hsu, D.; and Lee, W. 2009. Motion Planning under Uncertainty for Robotic Tasks with Long Time Horizons. In ISRR.
7. Kurniawati, H.; Hsu, D.; and Lee, W. 2008. SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces. In RSS.
8. McGovern, A., and Barto, A. 2001. Automatic discovery of subgoals in reinforcement learning using diverse density. In ICML.
9. McGovern, A. 1998. acQuire-macros: An algorithm for automatically learning macro-actions. In NIPS AHRL.
10. Pineau, J.; Gordon, G.; and Thrun, S. 2003. Policy-contingent abstraction for robust robot control. In UAI.
11. Puterman, M. 1994. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, Inc. New York, NY, USA.
12. Ross, S.; Pineau, J.; Paquet, S.; and Chaib-draa, B. 2008. Online planning algorithms for POMDPs. JAIR 32:663-704.
13. Smith, T., and Simmons, R. 2004. Heuristic search value iteration for POMDPs. In UAI.
14. Stolle, M., and Precup, D. 2002. Learning options in reinforcement learning. Lecture Notes in Computer Science.
15. Sutton, R.; Precup, D.; and Singh, S. 1999. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial intelligence 112(1): 181-211.
16. Theocharous, G., and Kaelbling, L. 2003. Approximate planning in POMDPs with macro-actions. NIPS.
17. Toussaint, M.; Charlin, L.; and Poupart, P. 2008. Hierarchical POMDP controller optimization by likelihood maximization. In UAI.
18. Yu, C; Chuang, J.; Computing, S.; Math, C; Gerkey, B.; Gordon, G.; and Ng, A. 2005. Open-loop plans in multi-robot POMDPs. Technical report, Stanford CS Dept.