A 25-year-old book about science has some explaining to do. Machines Who Think was conceived as a history of artificial intelligence, beginning with the first dreams of the classical Greek poets (and the nightmares of the Hebrew prophets), up through its realization as twentieth-century science. The interviews with AI's pioneer scientists took place when the field was young and generally unknown. They were nearly all in robust middle age, with a few decades of fertile research behind them, and luckily, more to come. Thus their explanations of what they thought they were doing were spontaneous, provisional, and often full of glorious fun.

A program called "AM" is described which carries on simple mathematics research,defining and studying new concepts under the guidance of a large body ofheuristic rules. The 250 heuristics communicate via an agenda mechanism, aglobal priority queue of small tasks for the program to perform, and reasons whyeach task is plausible (for example, "Find generalizations of 'primes', because'primes' turned out to be so useful a concept"). Each concept is represented asan active, structured knowledge module. One hundred very incomplete modulesare initially supplied, each one corresponding to an elementary set-theoreticconcept (for example, union). This provides a definite but immense space whichAM begins to explore. In one hour, AM rediscovers hundreds of common concepts(including singleton sets, natural numbers, arithmetic) and theorems (for example,unique factorization).Summary of Ph.D. dissertation.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

Cognitive Science 3(3):177-212.

CACM, 22 (10), 560-70.

Artificial Intelligence, 12 (1), 23-40.

In H. O'Neil (Ed.), Procedures for instructional systems development. New York: Academic Press, 273-313.

... we began to program a computer understanding system thatwould attempt to process input texts. An item crucial to our ability to accomplishthis task was what we called a script. A script is a frequently repeated causalchain of events that describes a standard situation. In understanding, when it ispossible to notice that one of these standard event chains has been initiated,then it is possible to understand predictively. That is, if we know we are in arestaurant then we can understand where an "order" fits with what we justheard, who might be ordering what from whom, what preconditions (menu,sitting down) might have preceded the "order", and what is likely to happennext. All this information comes from the restaurant script.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

In this paper we have shown how it is possible to use certain combinators onrelations to produce an interpretation of a class of clauses (Horn Clauses) inpredicate logic. The work was inspired by a particular view of the task of writingcertain kinds of program, but has not yet given rise to a system implementedon a digital computer, although some initial studies have been made.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.

Technometrics, 21(2)

Proceedings of the Sixth International Joint Conference on Artificial Intelligence, Cambridge, Mass., 645-655