The old Reifying Robots reading group page is here.
Reading List for Richer Representations group.
Uncategorized.
Deictic Representations.
- Agre, P.E., The dynamic structure of everyday life.
MIT Artificial Intelligence Laboratory. Technical report No. 1085
(Ph.D. thesis), 1988.
- Agre, Philip E., and Chapman, David. Indexicality and the Binding Problem, Proceedings of the AAAI Symposium: How can slow components think so fast?, 1988.
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Ballard, D. H., Hayhoe, M. M., Pook, P.K., and Rao, R. P. N.,
Deictic codes for the embodiment of cognition. Behavioral and Brain
Sciences 20:723-767, 1997.
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Foner, L., and Maes, P., Paying attention to what's important:
Using focus of attention to improve unsupervised learning. In
Proceedings of the Third International Conference on Simulation of
Adaptive Behavior. Brighton, UK: MIT Press, 1994.
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Martin, Mario, Reinforcement Learning for Embedded Agents Facing Complex Tasks, PhD thesis, Universitat Politecnica Catalunya, 1998.
- Whitehead, Steven D. and Ballard, Dana H., Learning to Perceive and
Act by Trial and Error. Machine Learning, 1991.
Reinforcement Learning Algorithms.
Learning Models.
Learning Automata.
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Angluin, D. Learning regular sets from queries and counterexamples. Information and Computation, 75(2):87--106, 1987.
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Rivest, R. L., and Schapire R. E. Inference of finite automata using homing sequences. Information and Computation, 103(2):299--347, Apr. 1993.
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Rivest, R., and Schapire, R. Diversity-based Inference of Finite Automata, in Jour. of the ACM, Vol. 4, pp. 555-589, 1994.
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Littman, M.L., Sutton, R.S., Singh, S., Predictive Representations
of State, (submitted) 2001
Incorporating Uncertainty into Model Building or Learning
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Dean, T.; Givan, R.; and Leach, S. 1997. Model reduction techniques
for computing approximately optimal solutions for Markov decision
processes. In Geiger, D., and Shenoy, P. P., eds., Proceedings of the
13th Conference on Uncertainty in Artificial Intelligence (UAI97) ,
124--131. San Francisco: Morgan Kaufmann Publishers.
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Dearden, R., Friedman, N. & Russell, S. (1998), Bayesian
Qlearning, in `Proceedings of the Fifteenth National Conference on
Artificial Intelligence (AAAI-98)'.
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Givan, R.; Leach, S.; and Dean, T. 1997. Bounded parameter Markov
decision processes. In Steel, S., and Alami, R., eds., Proceedings
of the 4th European Conference on Planning (ECP-97): Recent Advances
in AI Planning, volume 1348 of LNAI, 234--246. Berlin: Springer.
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Kearns, M., and Singh, S., "Bias-Variance" Error Bounds for
Temporal Difference Updates. COLT 2000.
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Koller, Daphne, and Parr, Ronald, Computing Factored Value
Functions for Policies in Structured MDPs. Proceedings of the
Sixteenth International Joint Conference on Artificial Intelligence
(IJCAI 1999).
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Rodriguez, A. C.,Parr, R., and D. Koller, D., Reinforcement
learning using approximate belief states. Advances in Neural Information
Processing Systems (NIPS), Denver, Colorado, December 1999.
Rule Learning.
- Drescher, Gary L.,"Made-Up Minds: A Constructivist Approach to Artificial
Intelligence". MIT Press, 1991 (Drescher thesis)
Decision Tree Extensions.
Vision.
- Chapman, D., "Vision Instruction and Action," The MIT Press,
Cambridge, MA, 1991.
- Ullman, Shimon, Visual Routines. MIT AI Memo 723,
1983.