Learning agents for uncertain environments (extended abstract)

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Learning agents for uncertain environments (extended abstract)

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Annual Workshop on Computational Learning Theory archive
Proceedings of the eleventh annual conference on Computational learning theory table of contents

Madison, Wisconsin, United States

Pages: 101 - 103

Year of Publication: 1998

ISBN:1-58113-057-0

Author

Stuart Russell

Computer Science Division, University of California, Berkeley, CA

Sponsors

University of Wisconsin : University of Wisconsin
UC @ Santa Cruz : UC @ Santa Cruz
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 29, Citation Count: 2

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	2	Dimitri P. Bertsekas , John N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996
	3	John Binder , Daphne Koller , Stuart Russell , Keiji Kanazawa, Adaptive Probabilistic Networks with Hidden Variables, Machine Learning, v.29 n.2-3, p.213-244, Nov./Dec. 1997 [doi>10.1023/A:1007421730016]
	4	Binder, J., Murphy, K., & Russell, S. (1997b). Space-efficient inference in dynamic probabilistic networks. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97) Nagoya, Japan. Morgan Kaufmann.
	5	Boyen, X., & Koller, D. (1998). Tractable inference for complex stochastic processes. In Proc. 14th Annual Conference on Uncertainty in AI (UAD. to appear.
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	7	Doya, K., & Sejnowski, T. (1995). A novel reinforcement model of birdsong vocalization learning. In Tesauro, G., Touretzky, D., & Leen, T (Eds.), Advances in Neural Information Processing Systems, Vol. 8, pp. 101-8 Denver, CO. M1T Press.
	8	Farley, C. T., & Taylor, C. R. (1991). A mechanical trigger for the trot-gallop transition in horses. Science, 253(5017), 306- 308.
	9	Friedman, N., Murphy, K., & Russell, S. (1998). Leaming the structure of dynamic probabilistic networks. In Uncertainty in Artificial Intelligence : Proceedings of the Fourteenth Conference Madison, Wisconsin. Morgan Kaufmann.
	10	Hoyt, D., & Taylor, C. (1981). Gait and the energetics of locomotion in horses. Nature, 292,239-240.
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	12	Kanazawa, K., Koller, D., & Russell, S. (1995). Stochastic simulation algorithms for dynamic probabilistic networks. In Eleventh Conference, pp. 346-351 Montreal, Canada. Morgan Kaufmann.
	13	Keeney, R. L., & Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York.
	14	McCallum, A. R. (1993). Overcoming incomplete perception with utile distinction memory. In Proceedings of the Tenth International Conference on Machine Learning, pp. 190-196 Amherst, Massachusetts. Morgan Kaufmann.
	15	Montague, P. R., Dayan, P., Person, C., & Sejnowski, T. J. (1995). Bee foraging in uncertain environments using predictive hebbian learning. Nature, 377, 725-728.
	16	Parr, R., & Russell, S. (1995). Approximating optimal policies for partially observable stochastic domains. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95) Montreal, Canada. Morgan Kaufmann.
	17	Ronald Parr , Stuart Russell, Reinforcement learning with hierarchies of machines, Proceedings of the 1997 conference on Advances in neural information processing systems 10, p.1043-1049, July 1998, Denver, Colorado, United States
	18	Stuart J. Russell , Peter Norvig, Artificial intelligence: a modern approach, Prentice-Hall, Inc., Upper Saddle River, NJ, 1995
	19	Rust, J. (1994). Do people behave according to bellman's principal of optimality?. Submitted to Journal of Economic Perspecfives.
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	21	Nestor A. Schmajuk , B. Silvano Zanutto, Escape, avoidance, and imitation: a neural network approach, Adaptive Behavior, v.6 n.1, p.63-129, June 1997 [doi>10.1177/105971239700600103]
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CITED BY 2

	Brenna D. Argall , Sonia Chernova , Manuela Veloso , Brett Browning, A survey of robot learning from demonstration, Robotics and Autonomous Systems, v.57 n.5, p.469-483, May, 2009
	Deepak Ramachandran , Eyal Amir, Bayesian inverse reinforcement learning, Proceedings of the 20th international joint conference on Artifical intelligence, p.2586-2591, January 06-12, 2007, Hyderabad, India