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Литература

201. Bryson A. E. and Ho Y.-C. (1969) Applied Optimal Control. Blaisdell, New York.

202. Buchanan B. G. and Mitchell Т. М. (1978) Model-directed learning of production rules. In WatermanD. A. and Hayes-Roth F. (Eds.), Pattern-Directed Inference Systems, p. 297—312. Academic Press, New York.

203. Buchanan B. G., Mitchell T. M., Smith R. G., and Johnson С R. (1978) Models of learning systems.In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker, New York.

204. Buchanan B. G. and Shortliffe E. H. (Eds.) (1984) Rule-Based Expert Systems: The MYCINExperiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, Massachusetts.

205. Buchanan B. G., Sutherland G. L., and Feigenbaum E. A. (1969) Heuristic DENDRAL: A programfor generating explanatory hypotheses in organic chemistry. In Meltzer В., Michie D., and Swann M. (Eds.), Machine Intelligence 4, p. 209-254. Edinburgh University Press, Edinburgh, Scotland.

206. Bundy A. (1999) A survey of automated deduction. In Wooldridge M. J. and Veloso M. (Eds.),Artificial intelligence today: Recent trends and developments, p. 153-174. Springer-Verlag, Berlin.

207. Bunt H. С. (1985) The formal representation of (quasi-) continuous concepts. In Hobbs J. R. andMoore R. C. (Eds.), Formal Theories of the Commonsense World, chap. 2, p. 37—70. Ablex, Norwood, New Jersey.

208. Burgard W., Cremers А. В., Fox D., Halinel D., Lakemeyer G., Schulz D., Steiner W., and Thrun S. (1999)Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1-2), p. 3-55.

209. Buro M. (2002) Improving heuristic mini-max search by supervised learning. Artificial Intelligence,134(1-2), p. 85-99.

210. Burstall R. M. (1974) Program proving as hand simulation with a little induction. In InformationProcessing 14, p. 308-312. Elsevier/North-Holland, Amsterdam, London, New York.

211. Burstall R. M. and Darlington J. (1977) A transformation system for developing recursive programs.Journal of the Association for Computing Machinery, 24(1), p. 44-67.

212. Burstein J., Leacock C, and Swartz R. (2001) Automated evaluation of essays and short answers. InFifth International Computer Assisted Assessment (CAA) Conference, Loughborough U.K. Loughborough University.

213. Bylander T. (1992) Complexity results for serial decomposability. In Proceedings of the Tenth NationalConference on Artificial Intelligence (AAAI-92), p. 729-734, San Jose. AAA! Press.

214. Bylander T. (1994) The computational complexity of propositional strips planning. ArtificialIntelligence, 69, p. 165-204.

215. Calvanese D., Lenzerini M, and Nardi D. (1999) Unifying class-based representation formalisms.Journal of Artificial Intelligence Research, 11, p. 199—240.

216. Campbell M. S., Hoane A. J., and Hsu F.-H. (2002) Deep Blue. Artificial Intelligence, 134(1-2),p. 57-83.

217. Canny J. and Reif J. (1987) New lower bound techniques for robot motion planning problems. IEEESymposium on Foundations of Computer Science, p. 39-48.

218. Canny J. (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysisand Machine Intelligence (PAMI), 8, p. 679-698.

219. Canny J. (1988) The Complexity of Robot Motion Planning. MIT Press, Cambridge, Massachusetts.

220. Carbonell J. G. (1983) Derivational analogy and its role in problem solving. In Proceedings of theNational Conference on Artificial Intelligence (AAAI-83), p. 64—69, Washington, DC. Morgan Kaufman n.