JACM, 29 (1), 24-32.

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

Discourse Processes, vol. 5, No. 1, pp. 1-32.

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.

In AAAI-82, pp. 155–158.

This paper describes XSEL, a program being developed at Carnegie-Mellon University that will assist salespeople in tailoring computer systems to fit the needs of customers. XSEL will have two kinds of expertise: it will know how to select hardware and software components that fulfil the requirements of particular sets of applications, and it will know how to provide satisfying explanations in the computer system sales domainIn 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.

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.