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81. Baum E., Boneh D., and Garrett C. (1995) On genetic algorithms. In Proceedings of the Eighth AnnualConference on Computational Learning Theory (COLT-92), p. 230-239, Santa Cruz, California. ACM Press.

82. Baum E. and Haussler D. (1989) What size net gives valid generalization? Neural Computation, 7(1),p. 151-160.

83. Baum E. and Smith W. D. (1997) A Bayesian approach to relevance in game playing. ArtificialIntelligence, 97(1-2), p. 195-242.

84. Baum E. and Wilczek F. (1988) Supervised learning of probability distributions by neural networks. InAnderson D. Z. (Ed.), Neural Information Processing Systems, p. 52-61. American Institute of Physics, New York.

85. Baum L. E. and Petrie T. (1966) Statistical inference for probabilistic functions of finite state Markovchains. Annals of Mathematical Statistics, 41.

86. Baxter J. and Bartlett P. (2000) Reinforcement learning in POMDP's via direct gradient ascent. InProceedings of the Seventeenth International Conference on Machine Learning, p. 41-48, Stanford, California. Morgan Kaufmann.

87. Bayardo R. J. and Schrag R. С (1997) Using CSP look-back techniques to solve real-world SATinstances. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), p. 203-208, Providence, Rhode Island. AAAI Press.

88. Bayes T. (1763) An essay towards solving a problem in the doctrine of chances. PhilosophicalTransactions of the Royal Society of London, 53, p. 370-418.

89. Beal D. F. (1980) An analysis of minimax. In Clarke M. R. B. (Ed.), Advances in Computer Chess 2,p. 103-109. Edinburgh University Press, Edinburgh, Scotland.

90. Beal D. F. (1990) A generalised quiescence search algorithm. Artificial Intelligence, 43(\), p. 85-98.

91.Beckert В. and Posegga J. (1995) Leantap: Lean, tableau-based deduction. Journal of Automated Reasoning, 75(3), p. 339-358.

92. Beeri C, Fagin R., Maier D., and Yannakakis M. (1983) On the desirability of acyclic databaseschemes. Journal ofthe Association forComputing Machinery, 30(3), p. 479-513.

93. Bell С and Tate A. (1985) Using temporal constraints to restrict search in a planner. In Proceedings ofthe Third Alvey IKBS SIG Workshop, Sunningdale, Oxfordshire, UK. Institution of Electrical Engineers.

94. Bell J. L. and Machover M. (1977) A Course in Mathematical Logic. Elsevier/North-Holland,Amsterdam, London, New York.

95. Bellman R. E. (1978) An Introduction to Artificial Intelligence: Can Computers Think? Boyd & FraserPublishing Company, San Francisco.

96. Bellman R. E. and Dreyfus S. E. (1962) Applied Dynamic Programming. Princeton University Press,Princeton, New Jersey.

97. Bellman R. E. (1957) Dynamic Programming. Princeton University Press, Princeton, New Jersey.

98. Belongie S., Malik J., and Puzicha J. (2002) Shape matching and object recognition using shape contexts.IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(A), p. 509—522.

99. Bender E. A. (1996) Mathematical methods in artificial intelligence. IEEE Computer Society Press, LosAlamitos, California.

100. Bentham J. (1823) Principles of Morals and Legislation. Oxford University Press, Oxford, UK. Originalwork published in 1789.