Institut Blaise Pascal, C.N.R.S., 23, Rue du Maroc, 75, Paris XIX, France We study some aspects of a general game-playing program. Such a program receives as data the rules of a game: an algorithm enumerating the moves and an algorithm indicating how to win. The program associates to each move the conditions necessary for this move to occur. It must find how to avoid a dangerous move. We describe the part of the program playing the combinatorial game in order to win: how it can find the moves which lead to victory and what are the only opponent's moves with which he does not lose. This program has been tried with various games: chess, tictac-too, etc. 1. INTRODUCTION My aim was to realize a program playing several games. The rules of the particular game which it must play are given as data. If we want to have a performing program, it must be capable of studying these rules.

In Meltzer, Bernard and Donald Michie (Eds.). Machine Intelligence 5, pp. 78-98. Edinburgh University Press.

"A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility. The computer program is heuristic in that it searches for plausible hypotheses in a small subset of the total hypothesis space according to heuristic rules learned from chemists."In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209-254. Edinburgh University Press

This paper shows how a question-answering system can be constructed usingfirst-order logic as its language and a resolution-type theorem-prover as itsdeductive mechanism. A working computer-program, Q A3, based on theseideas is described. The performance of the program compares favorably withseveral other general question-answering systems.Reprinted in B. L. Webber and N. J. Nilsson (eds.), Readings in Artificial Intelligence, pp, 202-222, San Francisco: Morgan Kaufmann, 1981 Machine Intelligence 4, pp. 183-205 Meltzer, B. and Michie, D.(eds.). Edinburgh: Edinburgh University Press.

Science 166:178-192

This is a reprint of a chapter that first appeared in 1968 in a collection of papers on operations research [1]. The chapter was written to survey the progress in heuristic processes since an earlier 1968 paper on the same topic [2]. In the current collection, it serves as something of a historical introduction to the SOAR system, which occupied Newell and his students for many years. Even though SOAR did not exist at the time this chapter was written, one finds some of the threads that are later to be drawn together into the construction of SOAR. Newell explores so-called weak methods, which trade power for general applicability.

In this section we discuss how theorem-proving methods are being tested for several applications in the Stanford Research Institute Artificial Intelligence Group's Automaton (robot). We emphasize that this section describes work that is now in progress, rather than work that is completed. These methods represent explorations in problem solving, rather than final decisions about how the robot is to do problem solving. An overview of the current status of the entire SRI robot project is provided by Nilsson. Coles has developed an English-to-logic translator that is part of the robot.

Summary This paper aescriDes a program, called "PRUW", which writes programs. PROW accepts the specification of the program in the language of predicate calculus, decides the algorithm for the program and then produces a LISP program which is an implementation of the algorithm. Since the construction of the algorithm is obtained by formal theorem-proving techniques, the programs that PROW writes are free from logical errors and do not have to be debugged. The user of PROW can make PROW write programs in languages other than LISP by modifying the part of PROW that translates an algorithm to a LISP program. Thus PROW can be modified to write programs in any language.

COMPUTER SOLUTION OF CALCULUS WORD PROBLEMS* Eugene Charniak Massachusetts Ins:itute of Technology Cambridge, Massachusetts SUMMARY A program was written to solve calculus word problems. The program, CARPS (CAlculus Rate Problem Solver), is restricted to rate problems. The overall plan of the program is similar to Bobrow's STUDENT, the primary difference being the introduction of "structures" as the internal model in CARPS. Structures are stored internally as trees, each structure holding the information gathered about one object. It was found that the use of structures made CARPS more powerful than STUDENT in several respects. In calculus word problems it is not uncommon to have two or three sentences providing information for one equation. For example, in a problem about a filter, ALTITUDE was interpreted as ALTITUDE OF THE FILTER because CARPS knew that since the filter was a cone and cones have altitudes the filter had an altitude. The program has solved 14 calculus problems, most taken (sometimes with slight modifications) from standard calculus texts. CARPS is written in two languages. The bulk of the coding is in LISP.

This term is meant to suggest not only the usual specifics of programming system and language but also such more elusive and subjective considerations as ease and level of interaction, "forgivefulness" of errors, human engineering, and system "Initiative." In normal usage, the word "environment" refers to the "aggregate of social and cultural conditions that influence the life of an individual." The programmer's enivronment influences, to a large extent determines, what sort of problems he can (and will want to) tackle, how far he can go, and how fast. If the environment is "cooperative" and "helpful" -- the anthropomorphism is deliberate -- then the programmer can be more ambitious and oroductive. If not, he will spend most of his time and energy "fighting" the system, which at times seems bent on frustrating his best efforts.