Country
Preference semantics
In E. L. Keenan (Ed.), Formal semantics of natural language. Cambridge, England: Cambridge University Press, 329-348.
Frame representations and the procedural/declarative controversy
In D. G. Bobrow and A. Collins (Eds.), Representation and understanding: studies in cognitive science. New York: Academic Press, 185-210. Also In: Ronald Brachman and Hector Levesque, editors, Readings in Knowledge Representation, pages 357-370. Morgan
Reasoning from incomplete knowledge in a procedural deductive system
Also see book version (1980), IJCAI-77. Also Rep. No. TR-347, AI Laboratory, Massachusetts Institute of Technology.
Semantic networks and case grammar
Publication 29, Institute of Linguistics, University of Stockholm.
Adaptation in Natural and Artificial Systems
John Holland's pioneering book Adaptation in Natural and Artificial Systems [1975, 2nd ed. 1992] showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm transforms a population of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and naturally occurring genetic operations such as crossover (recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good or best solution to the problem by genetically breeding the population of individuals. In preparing to use the conventional genetic algorithm operating on fixed-length character strings to solve a problem, the user must 1. determine the representation scheme, Ann Arbor: The University of Michigan Press
Prolog: Manual de reference et d’utilization
Tech. rep., Groupe d’Intelligence Artificielle, Universit´e d’Aix-Marseille.
Analysing mathematical proofs (or reading between the lines)
John Wiley and Sons, Inc. Robinson, J.A. (1965) "A Machine Oriented Logic Based on the Resolution Principle".
Speech Recognition: Invited Papers Presented at the IEEE Symposium
New York: Academic Press
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
See also Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY: John Wiley & Sons, Inc. Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature323 (6088): 533–536. doi:10.1038/323533a0.Ph.D. thesis, Harvard University.