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Ernest S. Davis

Associate Professor of Computer Science
Ph.D., Yale University

Professor Davis studies the problem of representing commonsense knowledge for artificial intelligence programs. To behave as humans do in real-world situations, an AI program must be endowed with the basic knowledge of the world that is common to humans. To achieve this, the programmer must analyze the relevant commonsense knowledge being used; express this knowledge in systematic data structures; and providing the program with techniques for effectively using that knowledge. Davis' research touches on a wide range of commonsense domains, including space, time, physical behavior on the human scale, mental attitudes, and planning. He also studies the application to commonsense reasoning of methods of plausible inference, such as non-monotonic logic and Bayesian probability theory.

  1. E. Davis, (1990). Representations of Commonsense Knowledge, Morgan Kaufmann, San Mateo, CA.

  2. E. Davis, (1993). ``The Kinematics of Cutting Solid Objects,'' Annals of Mathematics and Artificial Intelligence, 9:253-305.

  3. E. Davis, (1994). ``Knowledge Preconditions for Plans,'' Journal of Logic and Computation, 4(5):721-766.