Reddy, C. and Tadepalli, P. Learning Relational Rules for Goal Decmposition Machine Learning submitted, Postscript preprint.
Tadepalli, P. Cognitive Architectures have Limited Explanatory Power Commentary on Anderson and Lebiere's article on ``The Newell Test for a Theory of Mind,'' in Behavioral and Brain Sciences. To appear.
Amoth, T., Cull, P. and Tadepalli, P. On Exact Learning of Unordered Tree Patterns. Machine Learning , 44(3), 211--243, 2001.
Reddy, C. and Tadepalli, P. Learning Horn Definitions: Theory and an Application to Planning. New Generation Computing , Postscript preprint 17, 77--98, 1999.
Tadepalli, P. and Ok, D. Model-based Average Reward Reinforcement Learning. Postscript preprint Artificial Intelligence , 100, 177--224, 1998.
Tadepalli, P. and Natarajan, B. A Formal Framework for Speedup Learning from Problems and Solutions. Postscript preprint Journal of AI Research 4, 445-475, 1996.
Tadepalli, P. and Russell, S. Learning from Queries and Examples with Structured Determinations. Postscript preprint Machine Learning , 32, 245--295, 1998.
Mahadevan, S. and Tadepalli, P., Quantifying Prior Determination Knowledge using PAC Learning Model. Machine Learning , 17, 69-105, 1994. Postscript preprint
Mahadevan, S., Mitchell, T., Mostow, J., Stienberg, L. and Tadepalli, P. An Apprentice-based Approach to Knowledge Acquisition. Artificial Intelligence. 64, 1-52, 1993.
Weiss, S., Galen, R., and Tadepalli, P., Maximizing the Predictive Value of Production Rules. Artificial Intelligence. 45, 47-71, 1990.
Hall, P.R., Falkenhainer, B., Flann, N. S., Hampson, S., Reinke, R., Shrager, J., Sims, M., and Tadepalli, P. A review of the fourth international workshop on machine learning. Machine Learning. 2 (2), 1987.
Tadepalli, P., Givan, R. and Driessens, K. Relational Reinforcement Learning: An Overview, pdf preprint
Roncagliolo, S. and Tadepalli, P. Function Approximation in Hierarchical Relational Reinforcement Learning , pdf preprint
Seri, S. and Tadepalli, P. Model-based Hierarchical Average-reward Reinforcement Learning Postscript preprint, International Conference on Machine Learning , 2002.
Chisholm, M. and Tadepalli, P. Learning Decision Rules by Randomized Iterative Local Search Postscript preprint, International Conference on Machine Learning , 2002.
Amoth, T., Cull, P. and Tadepalli, P. Exact Learning of Unordered Tree Patterns from Queries. Postscript preprint Computational Learning Theory Conference , 1999.
Amoth, T., Cull, P. and Tadepalli, P. Exact Learning of Tree Patterns from Queries and Counterexamples. Postscript preprint Computational Learning Theory Conference , 1998.
Reddy, C., Tadepalli, P. Learning First-Order Acyclic Horn Programs from Entailment. Postscript preprint International Conference on Inductive Logic Programming , also appeared in International Conference on Machine Learning , 1998.
Reddy, C. and Tadepalli, P. Learning Horn Definitions using Equivalence and Membership Queries. Postscript preprint International Conference on Inductive Logic Programming , 1997.
Tadepalli, P. and Dietterich, T. G. Hierarchical Explanation-Based Reinforcement Learning. Proceedings of International Machine Learning Conference, 1997. Postscript preprint
Liere, R. and Tadepalli, P. Active Learning with Committees for Text Categorization . Proceedings of AAAI-97, Providence, RI, 1997 Postscript preprint, 591--596
Reddy, C., Tadepalli, P. Learning Goal-Decomposition Rules using Exercises. Proceedings of International Conference on Machine Learning (ICML-1997). Postscript preprint
Tadepalli, P. and Ok, D. Scaling up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function. Proceedings of International Machine Learning Conference, 1996. Postscript preprint
Ok, D. and Tadepalli, P. Auto-exploratory Average Reward Reinforcement Learning. Proceedings of AAAI-96. Postscript preprint
Reddy, C., Tadepalli, P. and Roncagliolo, S. Theory-guided Empirical Speedup Learning of Goal Decomposition Rules. Proceedings of International Machine Learning Conference, 1996. Postscript preprint
Tadepalli, P., Learning from Queries and Examples with Tree-Structured Bias. Proceedings of International Machine Learning Conference, Amherst, MA, 1993. Postscript preprint
Tadepalli, P., A Theory of Unsupervised Speedup Learning. Proceedings of National Conference on Artificial Intelligence, SanJose, CA, 1992. Postscript preprint
Tadepalli, P. A Formalization of Explanation-Based Macro-operator Learning. International Joint Conference on Artificial Intelligence, Sydney, Australia, 1991. Postscript preprint
Tadepalli, P. Lazy Explanation-Based Learning: A Solution to the Intractable Theory Problem. Proceedings of International Joint Conference on Artificial Intelligence, Detriot, MI, 1989.
Natarajan, B. and Tadepalli, P. Two New Frameworks for Learning. In Proceedings of the International Machine Learning Conference, Ann Arbor, MI, 1988.
Mahadevan, S. and Tadepalli, P. On the Tractability of Learning from Incomplete Theories. Proceedings of the International Machine Learning Conference, Ann Arbor, MI, 1988.
Weiss, S., Galen, R., and Tadepalli, P. Optimizing the Predictive Value of Diagnostic Decision Rules. Proceedings of the National Conference of AAAI-87, Seattle, 1987.
Ray Liere and Prasad Tadepalli, The Use of Active Learning in Text Categorization, in Working Notes of the AAAI 1996 Spring Symposium on Machine Learning in Information Access, Stanford, CA, 1996. Postscript preprint
Tadepalli, P. and Ok, D. H-Learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward. Technical Report 94-30-1, Oregon State University, Department of Computer Science. Postscript file