AI in the 1980s and Beyond: An MIT Survey by William Eric Leifur Grimson, Ramesh S. Patil

By William Eric Leifur Grimson, Ramesh S. Patil

This choice of essays by means of 12 contributors of the MIT employees, presents an within document at the scope and expectancies of present learn in a single of the world's significant AI facilities. The chapters on man made intelligence, specialist structures, imaginative and prescient, robotics, and ordinary language offer either a wide review of present components of job and an evaluate of the sector at a time of significant public curiosity and quick technological growth. Contents: synthetic Intelligence (Patrick H. Winston and Karen Prendergast). KnowledgeBased platforms (Randall Davis). Expert-System instruments and methods (Peter Szolovits). scientific prognosis: Evolution of structures development services (Ramesh S. Patil). synthetic Intelligence and software program Engineering (Charles wealthy and Richard C. Waters). clever usual Language Processing (Robert C. Berwick). computerized Speech attractiveness and figuring out (Victor W. Zue). robotic Programming and synthetic Intelligence (Tomas Lozano-Perez). robotic arms and Tactile Sensing (John M. Hollerbach). clever imaginative and prescient (Michael Brady). Making Robots See (W. Eric L. Grimson). self reliant cellular Robots (Rodney A. Brooks). W. Eric L. Grimson, writer of From pictures to Surfaces: A Computational learn of the Human Early imaginative and prescient approach (MIT Press 1981), and Ramesh S. Patil are either Assistant Professors within the division of electric Engineering and desktop technological know-how at MIT. AI within the Eighties and past is integrated within the synthetic Intelligence sequence, edited by way of Patrick H. Winston and Michael Brady.

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Extra resources for AI in the 1980s and Beyond: An MIT Survey

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Many expert systems have been implemented to solve problems that are some form of diagnostic reasoning, ranging from medical diagnosis to troubleshooting generators, locomotives, and telephone networks. Many expert systems use simple if-then rules to express their bits of knowledge, with a uniform rule interpreter to link together such rules into more com­ plex, longer chains of inference. Much of the knowledge in expert systems 3My own preference is for the term "knowledge-based systems" because it focuses on the critical role of knowledge.

As one example, consider the following rule that we might find in a medical diagnosis system. 8) to be mononucleosis. In some sense this rule is "true" ; doctors have noticed the association often enough to know that it is is worth taking note of. But if we ask why it is true, we often find that the answer is something like, "The ultimate etiology is yet to be determined," (medical jargon for "Damned if we know" ) . The important points here are the associational character of the knowl­ edge, its lack of more fundamental explanation, and it typical origin: the accumulated experience of practitioners.

A disease is a class. This latest method is in fact close a general expression of what has been called abduction, the process of working backward from observable mani­ festations to their probable explanatory cause. In this view, then, expert systems provide a programming method­ ology that separates factual statements from the methods that determine how those facts will be used. Within this methodology, there remains the flexibility to have both very specific and very general facts and methods.

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