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).

Frogs and toads provide interesting parallels to the way in which humans can see the world about them, and use what they see in determining their actions. What they lack in subtlety of visually-guided behaviour, they make up for in the amenability of their behaviour and the underlying neural circuitry to experimental analysis. This paper presents three specific models of neural circuitry underlying visually-guided behaviour in frog and toad. They form an 'evolutionary sequence' in that each model incorporates its predecessor as a subsystem in such a way as to explain a wider range of behaviour data in a manner consistent with current neurophysiology and anatomy. The models thus form stages in the evolution of Rana computatrix, an increasingly sophisticated model of neural circuitry underlying the behaviour of the frog.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

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

A piece-of-advice suggests what goal should be achievednext while preserving some other condition. If this goal can be achieved in agiven problem-situation (e.g. a given chess position) then we say that the piece-ofadviceis 'satisfiable' in that position. In this way ALI makes it possible to breakthe whole problem of achieving an ultimate goal into a sequence of subproblems,each of them consisting of achievement of a subgoal prescribed by some pieceof-advice. The control structure which chooses what piece-of-advice to applynext consists of a set of 'advice-tables', each of them being specialized in acertain problem-subdomain.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

See also: Proceedings of the National Academy of Sciences USA 79, 2554-2558

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.

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.

The MIT Press.

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.

Available for a fee. Manuscript available at Carnegie Mellon University https://kilthub.cmu.edu/articles/A_world-championship-level_Othello_program/6602903. Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, Iago. The work described here includes: (1) a task analysis of Othello; (2) the implemenation of a program based on this analysis and state-of-the-art AI gameplaying techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play. Artificial Intelligence, 19, 279-320.