This paper presents the view that artificial intelligence (AI) is primarily concerned with propositional languages for representing knowledge and with techniques for manipulating these representations. In this respect, AI is analogous to applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the context being described by these languages. Notable exceptions involve "commonsense" knowledge about the everyday would ( no other specialty claims this subject area as its own ), and metaknowledge (or knowledge about the properties itself). In these areas AI is concerned with content as well as form. We also observe that the technology that seems to underly peripheral sensory and motor activities (analogous to low-level animal or human vision and muscle control) seems to be quite different from the technology that seems to underly cognitive reasoning and problem solving. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.

Consider a class of computing problem for which all bananas is left as an exercise for the reader, or the sufficiently short programs are too slow and all sufficiently monkey. When it has been possible to couple causal models problems of this kind were left strictly alone for the first with various kinds and combinations of search, twenty-years or so of the computing era. There were two mathematical programming and analytic methods, then good reasons. First, the above definition rules out both evaluation of t has been taken as the basis for "high road" the algorithmic and the database type of solution. In "low road" representations Second, in a pinch, a human expert could usually be s may be represented directly in machine memory as a set found who was able at least to compute acceptable A recent pattern-directed allocation, inventory optimisation, or whatever large heuristic model used for industrial monitoring and control combinatorial domain might happen to be involved.

The Rete Match Algorithm is an efficient method for comparing a large collection of patterns to a large collection of objects. It finds all the objects that match each pattern. The algorithm was developed for use in production system interpreters, and it has been used for systems containing from a few hundred to more than a thousand patterns and objects. This article presents the algorithm in detail. It explains the basic concepts of the algorithm, it describes pattern and object representations that are appropriate for the algorithm, and it describes the operations performed by the pattern matcher.

"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.

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.

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.

A knowledge-based programming system can utilize a very-high-level self description to rewrite and improve itself. This paper presents a specification, in the very-high-level language V, of the rule compiler component of the CIII knowledgebased programming system. From this specification of part of itself, CIII produces an efficient program satisfying the specification. This represents a modest application of a machine intelligence system to a real programming problem, namely improving one of the programming environment's tools — the rule compiler. The high-level description and the use of a programming knowledge base provide potential for system performance to improve with added knowledge.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus.Proc AAAI

This paper has three themes: (1) The task of acquiring and organizing the knowledge on which to base an expert system is difficult.(2) Inductive inference systems can be used to extract this knowledge from data.(3) The knowledge so obtained is powerful enough to enable systems using it to compete handily with more conventional algorithm-based systems.These themes are explored in the context of attempts to construct high-performance programs relevant to the chess endgame king-rook versus king-knight.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

"An introductory chapter describes the historical and technical foundations of the work .... subsequent chapters describe five prototype computer programs that tackle difficult clinical problems in a manner similar to that of an expert physician. The programs presented are INTERNIST, a diagnostic aid that combines a large database of disease/manifestation associations with techniques for problem formulation; EXPERT and the Glaucoma Program which use physiological models for the diagnosis and treatment of eye disease; MYCIN, a rule-based program for diagnosis and therapy selection for infectious diseases; the Digitalis Therapy Advisor, which aids the physician in prescribing the right dose of the drug digitalis and also explains its actions; and ABEL, a program that uses multi-level pathophysiologic models for diagnosis of acid-base and electrolyte disorders."AAAS Selected Symposia Series, Volume 51. Available from MIT.