This article is a written version of an invited talk on artificial intelligence (AI) and the history of science that was presented at the Fifth National Conference on Artificial Intelligence (AAAI-86) in Philadelphia on 13 August 1986. Included is an expanded section on the concept of an abstraction in AI; this section responds to issues that were raised in the discussion which followed the oral presentation. The main point here is that the history of science can be used as a source for constructing abstract theory types to aid in solving recurring problem types. Two theory types that aid in forming hypotheses to solve adaptation problems are discussed: selection theories and instructive theories. Providing cases from which to construct theory types is one way in which to view the history of science as "complied hindsight" and might prove useful to those in AI concerned with scientific knowledge and reasoning.

Review of Expert Micros

We note with sorrow the passing of Don Waterman, who died on January 4, 1987. Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems").

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered. Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

To fully appreciate Professor Pearl's book, begin with a and the numerous techniques for representing knowledge careful reading of the title. It is a book about "..Intelligent-and uncertainty in common use in mainstream AI. ..Strategies.." for the discovery and use of "Heuristics.. " Chapter 5 begins a quantitative performance analysis of to allow computers to solve ".. Search.. ' ' problems. This includes a nice exposition on is a critical component in AI programs (Nilsson 1980, Barr branching processes, although the mathematically unsophisticated and Feigenbaum 1982), and in this sense Pearl's book is a reader may find it difficult. Here Pearl introduces strong contribution to the field of AI. It serves as an excellent probabilistic models to complement probabilistic heuristics. As a book about search, it is thorough, at analysis of search heuristics, and to a probabilistic analysis the state of the art, and contains expositions that will delight of nonadmissible heuristics in ...

Essentially a survey of the development of PC-based expert systems and a review of existing applications, languages, and shells, this book leaves many of the important questions unanswered.

Proc. Tenth Intl. Joint Conference on Artificial Intelligence (pp. 334-337). Milan, Italy: IJCAII.

IEEE Computer, pages 100-109, January

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