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Литература

1041.  Michalski R. S., Mozetic I., Hong J., and Lavrac, N. (1986b) The multi-purpose incrementallearning system aql5 and its testing application to three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), p. 1041-1045, Philadelphia. Morgan Kaufmann.

1042. Michel S. and Plamondon P. (1996) Bilingual sentence alignment: Balancing robustness and accuracy. InProceedings of the Conference of the Association for Machine Translation in the Americas (AMTA).

1043. Michie D. (1966) Game-playing and game-learning automata. In Fox L. (Ed.), Advances inProgramming and Non-Numerical Computation, p. 183-200. Pergamon, Oxford, UK.

1044. Michie D. (1972) Machine intelligence at Edinburgh. Management Informatics, 2(1), p. 7-12.

1045. Michie D. (1974) Machine intelligence at Edinburgh. In On Intelligence, p. 143-155. EdinburghUniversity Press.

1046. Michie D. and Chambers R. A. (1968) BOXES: An experiment in adaptive control. In Dale E. andMichie D. (Eds.), Machine Intelligence 2, p. 125-133. Elsevier/North-Holland, Amsterdam, London, New York.

1047. Michie D., Spiegelhalter D. J., and Taylor С (Eds.) (1994) Machine Learning, Neural and StatisticalClassification. Ellis Horwood, Chichester, England.

1048.  Milgrom P. (1997) Putting auction theory to work: The simultaneous ascending auction. Tech. rep.Technical Report 98-0002, Stanford University Department of Economics.

1049. Mill J. S. (1843) A System of Logic, Ratiocinative and Inductive: Being a Connected View of thePrinciples of Evidence, and Methods of Scientific Investigation. J. W. Parker, London.

1050. Mill J. S. (1863) Utilitarianism. Parker, Son and Bourn, London.

1051. Miller A. C, Merkhofer M. M., Howard R. A, Matheson J. E., and Rice T. R. (1976) Development ofautomated aids for decision analysis. Technical report, SRI International, Menlo Park, California.

1052. Minsky M. L. (Ed.) (1968) Semantic Information Processing. MIT Press, Cambridge, Massachusetts.

1053. Minsky M. L. (1975) A framework for representing knowledge. In Winston P. H. (Ed.), ThePsychology of Computer Vision, p. 211-277. McGraw-Hill, New York. Originally an MIT AI Laboratory memo; the 1975 version is abridged, but is the most widely cited.

1054. Minsky M. L. and Papert S. (1969) Perceptrons: An Introduction to Computational Geometry (firstedition). MIT Press, Cambridge, Massachusetts.

1055. Minsky M. L. and Papert S. (1988) Perceptrons: An Introduction to Computational Geometry(Expanded edition). MIT Press, Cambridge, Massachusetts.

1056. Minton S. (1984) Constraint-based generalization: Learning game-playing plans from singleexamples. In Proceedings of the National Conference on Artificial Intelligence (AAAI-84), p. 251-254, Austin, Texas. Morgan Kaufmann.

1057. Minton S. (1988) Quantitative results concerning the utility of explanation-based learning. InProceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), p. 564—569, St. Paul, Minnesota. Morgan Kaufmann.

1058. Minton S., Johnston M. D., Philips А. В., and Laird P. (1992) Minimizing conflicts: A heuristicrepair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58(1-3), p. 161-205.

1059. Mitchell M. (1996) An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts.

1060. Mitchell M., Holland J. H., and Forrest S. (1996) When will a genetic algorithm outperform hillclimbing? In Cowan J., Tesauro G., and Alspector J. (Eds.), Advances in Neural Information Processing Systems, Vol. 6. MIT Press, Cambridge, Massachusetts.