In every professional field there are large bodies of information acquired through study and experience by practitioners. In many fields, individuals can be identified whose performance consistently approaches the best. The goal of expert systems technology is to embody the experts' knowledge in some field within a computer. Then, the computer can act as an expert consultant for non-expert professionals or laymen. Existing systems, such as MYCIN [2], for diagnosing blood infections, or PROSPECTOR [3], for evaluating field sites for minable mineral deposits, can perform at a level exceeding that of the average practitioner in the field. These systems typically run on large, time-shared computers. There are two components to an expert system: the expert knowledge itself, and a'core' system for manipulating that knowledge and interacting with the user. General methodologies have been developed for encoding expert knowledge; the encoding is typically done by a computer scientist in close collaboration with an expert or experts from the field of specialization.

Equation Time Methods Used (I) 2200 Function Swapplng,Polysolve (2) 1905 Function Swapping,Isolation (3) 6280 Homogenization,Function Swapping, (4) I010 (5) 1350 (6) 815 (7) 3580 The numbered equations refer to given in milliseconds. Polysolve,Isolation Homogenization,Polysolve,Isolation Homogenization,Polysolve,Isolation Attraction,Collection,Isolation The following table Homogenlzation,Polysolve,Isolation those given in the introduction. Times are CPU times REFERENCES [Borning and Bundy 81] Borning, A and Bundy, A. Using matching in algebraic equation solving.

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as: (1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

ABSTRACT: Objects move, collide, flow, bend, heat up, cool down, stretch, compress . and boil. These and otherthings that cause changes in objects over time are intuitively characterized as processes . To understandcommonsense physical reasoning and make programs that interact with the physical world as well aspeople do we must understand qualitative reasoning about processes, when they will occur, theireffects, and when they will stop. Qualitative process theory defines a simple notion of physical processthat appears useful as a language in which to write dynamical theories. Reasoning about processesalso motivates a new qualitative representation for quantity in terms of inequalities, called thequantity space . This paper describes the basic concepts of qualitative process theory, several differentkinds of reasoning that can be performed with them, and discusses its implications for causalreasoning. Several extended examples illustrate the utility of the theory, including figuring out that aboiler can blow up, that an oscillator with friction will eventually stop, and how to say that you canpull with a string, but not push with it. Journal-length version of Ph.D. dissertation, , MIT, 1985.Artifiicial Intelligence. Also In Bobrow, D. (Ed.), Qualitative Reasoning About Physical Systems, pp. 85–186. MIT Press. Also in Artificial Intelligence 24:85-168 (1984).

This is the first presidential address of AAAI, the American Association for Artificial Intelligence. In the grand scheme of history of artificial intelligence (AI), this is surely a minor event. The field this scientific society represents has been thriving for quite some time. No doubt the society itself will make solid contributions to the health of our field. But it is too much to expect a presidential address to have a major impact. So what is the role of the presidential address and what is the significance of the first one? I believe its role is to set a tone, to provide an emphasis. I think the role of the first address is to take a stand about what that tone and emphasis should be-set expectations for future addresses and to communicate to my fellow presidents. Only two foci are really possible for a presidential address: the state of the society or the state of the science. I believe the latter to be correct focus. AAAI itself, its nature and its relationship to the larger society that surrounds it, are surely important. However, our main business is to help AI become a science -- albeit a science with a strong engineering flavor. Thus, though a president's address cannot be narrow or highly technical, it can certainly address a substantive issue. That is what I propose to do.

The Yale Artificial Intelligence Project, under the direction of Professor Roger C. Schank, supports a number of research projects. Most of this research is in the02-02 area of attempting to model the processes involved in human understanding, with a current emphasis on memory models and the processes involved in learning.

Advanced Information and Decision Systems (AI-DS) is a relatively new, employee-owned company that does basic and applied research, product development, and consulting in the fields of artificial intelligence, computer science, decision analysis, operations research, control theory, estimation theory, and signal processing. AI&DS performs studies, analyses, systems design and evaluation, and software development for a variety of industrial clients and government agencies, including the Department of Defense and Energy.

Bolt Beranek and Newman June 1981 was the closing date for the receipt of votes) The people listed below have been elected by the membership of the AAAI to the offices as indicated. AAAI Annual Meeting The election was special in several ways, in order to complete the initialization of officers and periods of tenure. The annual meeting of the AAAI will be held during the Both a president (for 1981-82) and a president-elect (who will IJCAI-RI meeting in Vancouver. The meeting will be held on serve as president for 1982-83) were elected. The IJCAI business president-elect would be on the ballot, however, no presidentelect meeting will also be held during the same period.

Carnegie-Mellon University has established a Robotics Institute to bring its expertise in engineering, science, and industrial administration to bear upon the problem of national industrial productivity. The institute has been established to undertake advanced research and development in seeing, thinking robots and intelligent systems, and to facilitate transfer of this technology to industry. The Institute is engaged in broad programs of research in robotics, artificial intelligence, manufacturing technology, micro-electronics technology, and computer science. The Institute offers the promise of dramatic advances that will not only improve the productivity of all types of employees but also lead to improvements in the "quality of life" for all.