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).

A ciear and powerful formalism for describing languages, both natural and artificial, follows from a method for expressing grammars in logic due to Colmerauer and Kowalski. This formalism, which is a natural extension of context-free grammars, we call “definite clause grammars” (DCGs). A DCG provides not only a description of a language, but also an effective means for analysing strings of that language, since the DCG, as it stands, is an executable program of the programming language Prolog. Using a standard Prolog compiler, the DCG can be compiled into efficient code, making it feasible to implement practical language analysers directly as DCGs. This paper compares DCGs with the successful and widely used augmented transition network (ATN) formalism, and indicates how ATNs can be translated into DCGs.

Englewood Cliffs, N.J.: Prentice-Hall

Our own early attempts (as devoted users of LISP) to use PROLOG convinced us that it would be worth the effort to create within LISP a faithful implementation of Kowalski's logic programming idea. We felt it would be very convenient to be able to set up a knowledge base of assertions inside a LISP workspace, and to compute the answers to queries simply by executing appropriate function calls.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

"Work on Expert Systems has received extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, asses the current prospects, and suggest directions appropriate for the next steps of basic research. I'd like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We'll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge. I guarantee we'll all return safely, so come along...." AI Magazine 3(2): Spring 1982, 3-22.

We propose a computational model for interpreting line drawings as three dimensional surfaces, based on constraints on local surface orientation along extremal and discontinuity boundaries. Specific techniques are described for two key processes: recovering the three-dimensional conformation of a space curve (e.g., a surface boundary) from its two-dimensional projection in an image, and interpolating smooth surfaces from orientation constraints along extremal boundaries.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

ABSTRACTIn many applied sciences there is often a problem of revealing a structure underlyinga given collection of objects (situations, measurements, observations, etc.).A specific problem of this type is that of determining a hierarchy of meaningfulsubcategories in such a collection. This problem has been studied intensively inthe area of cluster analysis. The methods developed there, however, formulatesubcategories ('clusters') solely on the basis of pairwise 'similarity' (or 'proximity')of objects, and ignore the issue of the 'meaning' of the clusters obtained. Themethods do not provide any description of the clusters obtained. This paperpresents a method which constructs a hierarchy of subcategories, such that anappropriately generalized description of each subcategory is a single conjunctivestatement involving attributes of objects and has a simple conceptual interpretation.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

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.