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).

In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

Motor control systems are complex systems that process information. Orientation behaviour, posture control, and the manipulation of objects are examples of motor control systems which involve one or more sensory modality and various central neural processes, as well as effector systems and their immediate neuronal control mechanisms. Like all complex information processing systems, they must be analysed and understood at several different levels (see, e.g., Marr & Poggio 1977).At the lowest level there is the analysis of basic components and circuits, the neurons, their synapses, etc. At the other extreme, there is the study of the computations performed by the system — the problems it solves and the ways that it solves them — and the analysis of its logical organization in terms of its primary modules. In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

It appears, however, that we [in AI] are now (finally!) on the verge of practicality in a number of specialities within machine intelligence more or less simultaneously. This can be expected to result in the short term in a qualitative shift in the nature of the field itself, and to result in the longer term in a shift in the way certain industries go about their businessThis paper will discuss three specific areas of work in machine intelligence that MIC [Machine Intelligence Corporation] feels are ripe for commercial application: machine vision, naturallanguage access to computers, and expert systems. It will close with some observations on what makes these areas appropriate for application at this time, and on the difference between a technical solution to a problem and a product.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

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.

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.

R1 is a rule-based program that configures VAX-11 computer systems. Given a customer's purchase order, it determines what, if any, substitutions and additions have to be made to the order to make it consistent and complete and produces a number of diagrams showing the spatial and logical relationships among the 90 or so components that typically constitute a system. The program has been used on a regular basis by Digital Equipment Corporation's manufacturing organization since January of 1980. R1 has sufficient knowledge of the configuration domain and of the percliarities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do; thus it requires little search in order to configure a computer system.

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.

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.