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Литература

1141. Nilsson N. J. (1971) Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York.

1142. Nilsson N. J. (1980) Principles of Artificial Intelligence. Morgan Kaufmann, San Mateo, California.

1143. Nilsson N. J. (1984) Shakey the robot. Technical note 323, SRI International, Menlo Park,California.

1144. Nilsson N. J. (1986) Probabilistic logic. Artificial Intelligence, 28(1), p. 71-87.

1145. Nilsson N. J. (1991) Logic and artificial intelligence. Artificial Intelligence, 47(1-3), p. 31-56.

1146. Nilsson N. J. (1998) Artificial Intelligence: A New Synthesis. Morgan Kaufmann, San Mateo,California.

1147. Norvig P. (1988) Multiple simultaneous interpretations of ambiguous sentences. In Proceedings of the10th Annual Conference of the Cognitive Science Society.

1148. Norvig P. (1992) Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp.Morgan Kaufmann, San Mateo, California.

1149. Nowick S. M., Dean M. E., Dill D. L., and Horowitz M. (1993) The design of a high-performance cachecontroller: A case study in asynchronous synthesis. Integration: The VLSI Journal, 75(3), p. 241-262.

1150. Nunberg G. (1979) The non-uniqueness of semantic solutions: Polysemy. Language and Philosophy,5(2), p. 143-184.

1151. Nussbaum M. С (1978) Aristotle's "De Motu Animalium". Princeton University Press, Princeton,New Jersey.

1152. Ogawa S., Lee T.-M., Kay A. R., and Tank D. W. (1990) Brain magnetic resonance imaging withcontrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, p. 9868-9872.

1153.01awsky D. and Gini M. (1990) Deferred planning and sensor use. In Sycara K. P. (Ed.), Proceedings, DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control, San Diego, California. Defense Advanced Research Projects Agency (DARPA), Morgan Kaufmann.

1154. Olesen K. G. (1993) Causal probabilistic networks with both discrete and continuous variables. IEEETransactions on Pattern Analysis and Machine Intelligence (PAMI), 15(3), p. 275—279.

1155. Oliver R. M. and Smith J. Q. (Eds.) (1990) Influence Diagrams, Belief Nets and Decision Analysis.Wiley, New York.

1156. Olson С F. (1994) Time and space efficient pose clustering. In Proceeding? of the IEEE Conference onComputer Vision and Pattern Recognition, p. 251-258, Washington, DC. IEEE Computer Society Press.

1157. Oncina J. and Garcia P. (1992) Inferring regular languages in polynomial update time. In Perez,Sanfeliu, and Vidal (Eds.) Pattern Recognition and Image Analysis, p. 49—61. World Scientific.

1158. O'Reilly U.-M. and Oppacher F. (1994) Program search with a hierarchical variable lengthrepresentation: Genetic programming, simulated annealing and hill climbing. In Davidor Y., Schwefel H.-P., and Manner R. (Eds.) Proceedings of the Third Conference on Parallel Problem Solving from Nature, p. 397-406, Jerusalem, Israel. Springer-Verlag.

1159. Ormoneit D. and Sen S. (2002) Kernel-based reinforcement learning. Machine Learning, 49(2—3),p. 161-178.

1160. Ortony A. (Ed.) (1979) Metaphor and Thought. Cambridge University Press, Cambridge, UK.