Research Report No. 25, Edinburgh: Department of Artificial Intelligence, August 1976.

Abstract: A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user’s view and access data according to the system’s view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query.IBM Journal of Research and Development 20:4, pp. 336-357.

Artificial Intelligence 7:21-49.

SRI Tech. Note 131, AI Center, SRI International, Inc., Menlo Park, Calif.

New York: Academic Press

The cerebellum provides precise coordination of motor control for such body parts as the eyes, arms, fingers, legs, and wings. It stores and retrieves information required to control thousands of muscles in producing coordinated behavior as a function of time. CMAC was designed to provide this kind of motor control for robotic manipulators. CMAC is a kind of memory, or table look-up mechanism, that is capable of learning motor behavior. It exhibits properties such as generalization, learning interference, discrimination, and forgetting that are characteristic of motor learning in biological creatures.

After examining the simplest statements thaï can be based on a verb, a noun and an adjective, we go on to more complex statements involving articles, relative clauses and négations. Emphasis is laid on the systematic transformation ofa sentence into a semantic formula, h appears that such semantic formulae can be interpre ted correctly only in a logical system wit h three truth-values. Moreover one must suppose that the elementary relations associâted wit h the verb s, nouns and adjective s range not on individuals, but on sets of individuals.

New York: Academic Press

 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

Northwestern students who are interested in changing their major, or simply sampling the computer science field, can take individual introductory courses.