Utile distinction hidden Markov models

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 855-862

  Wierstra, Daan ^a   Wiering, Marco ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION MAKING; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING;

HIDDEN MARKOV MODELS; HIDDEN STATES; INTERNAL STATES; STOCHASTIC PROBLEMS;

MARKOV PROCESSES;

EID: 14344255790     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Aberdeen, D. (2003). Policy-Gradient Algorithms for Partially Observable Markov Decision Processes. Doctoral dissertation, Research School of Information Science and Engineering, Australian National University.
2. Bakker, B. (2004). The State of Mind. Doctoral dissertation, Unit of Cognitive Psychology, Leiden University.
3. Bengio, Y., & Prasconi, P. (1995). An input/output HMM architecture. In G. Tesauro & D. Touretzky & T. Leen (Ed.), Advances in Neural Information Processing Systems 7 (pp. 427-434). Cambridge, MA: MIT Press.
4. Chrisman, L. (1992). Reinforcement learning with per-ceptual aliasing: The perceptual distinctions approach. Proceedings of the Tenth International Conference on Artificial Intelligence (pp. 183-188). San Jose, California: AAAI Press.
5. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4, 237-285.
6. Lin, L., & Mitchell, T. (1993). Reinforcement learning with hidden states. From animals to animals 2: Proceedings of the second international conference on simulation of adaptive behavior (pp. 271-280). Cambridge, MA: MIT Press.
7. Littman, M. L., Cassandra, A. R., & Kaelbling, L. P. (1995). Learning policies for partially observable environments: Scaling up. Proceedings of the Twelfth International Conference on Machine Learning (pp. 362-370). San Francisco: Morgan Kaufmann.
8. Loch, J., & Singh, S. (1998). Using eligibility traces to find the best memoryless policy in partially observable Markov decision processes. The Proceedings of the Fifteenth International Machine Learning Conference (pp. 141-150). San Francisco: Morgan Kaufmann.
9. McCallum, R. A. (1993). Overcoming incomplete perception with utile distinction memory. The Proceedings of the Tenth International Conference on Machine Learning (pp. 190-196). San Francisco: Morgan Kaufmann.
10. McCallum, R. A. (1995a). Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State. The Proceedings of the Twelfth International Conference on Machine Learning (pp. 387-395).
11. McCallum, R. A. (1995b). Reinforcement Learning with Selective Attention and Hidden State. Doctoral dissertation, Department of Computer Science, University of Rochester.
12. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2) (pp. 257-286).
13. Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
14. Whitehead, S. D., & Ballard, D. H. (1991). Learning to perceive and act by trial and error. Machine Learning, 7(1), 45-83.