Download e-book for iPad: 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 through 12 participants of the MIT employees, offers an inside of record at the scope and expectancies of present examine in a single of the world's significant AI facilities. The chapters on synthetic intelligence, professional structures, imaginative and prescient, robotics, and traditional language offer either a extensive review of present components of task and an overview of the sphere 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 platforms development services (Ramesh S. Patil). man made Intelligence and software program Engineering (Charles wealthy and Richard C. Waters). clever typical Language Processing (Robert C. Berwick). automated Speech popularity and knowing (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 sustaining cellular Robots (Rodney A. Brooks). W. Eric L. Grimson, writer of From photos to Surfaces: A Computational learn of the Human Early imaginative and prescient process (MIT Press 1981), and Ramesh S. Patil are either Assistant Professors within the division of electric Engineering and desktop technology at MIT. AI within the Nineteen Eighties and past is incorporated within the synthetic Intelligence sequence, edited via Patrick H. Winston and Michael Brady.

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In part then, the task of building a troubleshooter for any particular domain involves looking for a set of appropriate representations. Figure 14. The physical view of the circuit. Knowledge-Based Systems 37 These three ideas work together to support a fairly powerful form of diag­ nostic reasoning. The program can make simplifying assumptions in order to get started, back up when contradictions occur, and then try a slightly more elaborate version of the problem. 6 Design and Test Programming The bulk of our experience to date has been on various forms of diagnosis, simulation and test generation [Davis84, Shirley83, Hamscher84, Haig86].

Whether the program actually works in ways compa­ rable to those used by human experts or, indeed, whether its competence is that of an expert or only that of a thorough journeyman seems less significant. Nevertheless, I will use the more widely-used term "expert systems" here. Expert Systems Tools and Techniques 47 is heuristic in nature-it consists of rules of thumb that are often, but not always, true. Many systems reason by making assumptions that are then verified or rejected in later analysis.

1975, "A model of inexact reasoning in medicine," Mathematical Biosciences, 23, pp. 351-379. , 1985, Principle designed based on qualitative behavioral descriptions, MS Thesis, MIT, Computer Science. Expert Systems Tools and Techniques: Past, Present and Future Peter Szolovits My task in this article is to try to bring a little clarity to the discussion of tools for expert system construction. is still very much in flux, with new ideas and new products coming onto the stage constantly, and because our basis of practical experience is still small enough that it is not easy to separate the lasting ideas from the overblown publicity surrounding the commercialization of artificial intelli­ gence technology.

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