Steps Toward Artificial Intelligence*

8 8PROCEEDINGS OF THE IRE January Steps Toward Artificial Intelligence* MARVIN MINSKYt, MEMBER, IRE The work toward attaining "artificial intelligence" is the center of considerable computer research, design, and application. The field is in its starting transient, characterized by many varied and independent efforts. Marvin Minsky has been requested to draw this work together into a coherent summary, supplement it with appropriate explanatory or theoretical noncomputer information, and introduce his assessment of the state-of- the-art. This paper emphasizes the class of activities in which a general purpose computer, complete with a library of basic programs, is further programmed to perform operations leading to ever higher-level information processing functions such as learning and problem solving. This informative article will be of real interest to both the general PROCEEDINGS reader and the computer specialist. The Guest Editor Summary-The problems of heuristic programming-of making find only a few machines (mostly "general-purpose" computers solve really difficult problems-are divided into five main computers, programmed for the moment to behave ac- areas: Search, Pattern-Recognition, Learning, Planning, and Induction. cording to some specification) doing things that nmight A computer can do, in a sense, only what it is told to do. But even claim any real initellectual status. Some would be prov- when we do not know how to solve a certain problem, we may pro- ing mathematical theorems of rather undistinguished gram a machine (computer) to Search through some large space of character. A few machines might be playing certain solution attempts. Unfortunately, this usually leads to an enormously games, occasionally defeating their designers. Sonme inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's might be distinguishing between hand-printed letters. methods to appropriate problems. Pattern-Recognition, together Is this enough to justify so much interest, let alone deep with Learning, can be used to exploit generalizations based on ac- concern? I believe that it is; that we are on the thresh- cumulated experience, further reducing search. By analyzing the old of an era that will be strongly influenced, and quite situation, using Planning methods, we may obtain a fundamental possibly dominated, by intelligent problem-solving mla- improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, chines. But our purpose is not to guess about what the machines will need to construct models of their environments, using future may bring; it is only to try to describe and ex- some scheme for Induction. plain what seem nlow to be our first steps toward the Wherever appropriate, the discussion is supported by extensive construction of "artificial intelligence." citation of the literature and by descriptions of a few of the most Along with the development of general-purpose com- successful heuristic (problem-solving) programs constructed to date. puters, the past few years have seen an increase in effort INTRODUCTION toward the discovery and mechanizationi of problerm- a number of papers have ap- A VISITOR to our be about solving processes. Quite planet might puzzled theories or actual programs the role of in our On the peared describing comiiputer computers technology. concerned with pat- one hand, he would read and hear all about game-playing, theorem-provinig, wonderful "nmechaniical brains" baffling their creators tern-recognition, and other domainis which would seem with prodigious initellectual performance. And he (or it) to require some intelligence. The literature does not in- clude any general discussion of the outstanding prob- would be warned that these machines must be re- lems of this field. lest overwhelm us strained, they by might, persuasion, In this article, an attempt will be made t-o separate or even the revelation of truths too terrible to be by out, analyze, and find the relations between some of borne. On the other our visitor would find the hand, these problems. Analysis will be supported with enough machines being denounced, on all sides, for their slavish from the literal and examples literature to serve the introductory obedience, unimaginative interpretations, function of a review but there much for innovation or in for article, remains incapacity initiative; short, relevant work not here. This paper is highly their inhuman dullness. described and cannot to all Our visitor remain if he set out to find, compressed, therefore, begin discuss might puzzled these matters in the available for these monsters. For he would space. and judge himself, There is, of course, no generally accepted theory of is own * Received by the IRE, October 24, 1960. The author's work "intelligence"; the analysis our and mnay be con- summarized here which was done at Lincoln Lab., a center for re- troversial. We regret that we cannot give full personal search operated by M.I.T. at Lexington, Mass., with the joint sup- port of the U. S. Army, Navy, and Air Force under Air Force Con- acknowledgments here suffice it to say that we have tract AF 19(604)-5200; and at the Res. Lab. of Electronics, M.I.T., discussed these matters with almnost every one of the Cambridge, Mass., which is supported in part by the U. S. Army Signal Corps, the Air Force Office of Scientific Res., and the ONR- cited authors. is based on earlier work done by the author as a Junior Fellow of the It is convenient to divide the problems into five main Society of Fellows, Harvard Univ., Cambridge. t Dept. of Mathematics and Computation Center, Res. Lab. areas: Search, Pattern-Recognition, Learning, Plan- of Electronics, M.I.T., Cambridge, Mass. ning, and Iniduction; these comprise the main divisions 1961 Minsky: Steps Toward Artificial Intelligence 9 of the paper. Let us summarize the entire argument results of incomplete analysis can be used to mnake the very briefly: search more efficient. The necessity for this is simply A computer can do, in a sense, only what it is told to overwhelming: a search of all the paths through the do. But even when we do not know exactly how to solve game of checkers involves some 1040 move choices [21; a certain problem, we may program a machine to in chess, some 10120 [3]. If we organized all the particles Search through some large space of solution attempts. in our galaxy into some kind of parallel computer Unfortunately, wheni we write a straightforward pro- operating at the frequency of hard cosmic ravs, the lat- gram for such a search, we usually find the resulting ter computation would still take impossibly long; we process to be enormously inefficient. With Pattern- cannot expect improvements in "hardware" aloile to Recognition techniques, efficiency can be greatly im- solve all our problems! Certainly we must use whatever proved by restricting the machine to use its methods we know in advance to guide the trial generator. And only on the kind of attempts for which they are appro- we must also be able to make use of results obtained priate. And with Learning, efficiency is further improved alonig the way.23 by directing Search in accord with earlier experiences. By actually analyzing the situation, usinig what we call A. Relative Improvement, Hill-Climbing, and Heuristic Planning methods, the machinie may obtain a really Connections fundanmental improvemeint by replacing the originally given Search by a much smiialler, more appropriate ex- A problem can hardly come to interest us if we have ploration. Finally, in the section on Induction, we con- no backgroutnd of information about it. We usually sider some rather more global concepts of how one have some basis, however flimsy, for detecting improve- might obtain intelligenit machine behavior. ment; some trials will be judged more successful than others. Suppose, for example, that we have a comparator selects as the better, one from any pair of trial I. THE PROBLEM OF SEARCH1 which outcomes. Now the comparator cannot, alonie, serve to Summary-If, for a given problem, we have a means for checking make a problem well-defined. No goal is definied. But if a proposed solution, then we can solve the problem by testing all the relation betweeni trials is possible answers. But this always takes much too long to be of comparator-defined practical interest. Any device that can reduce this search may be of "transitive" (i.e., if A dominates B and B dominates C value. If we can detect relative improvement, then "hill-climbing" implies that A dominates C), then we can at least define (Section I-B) may be feasible, but its use requires some structural "progress," and ask our machine, given a time limit, to knowledge of the search space. And unless this structure meets cer- do the best it can. tain conditions, hill-climbing may do more harm than good. But it is essential to observe that a comparator by \Vheni we talk of problem-solvinig in what follows we itself, however shrewd, cannot alone give any imnprove- will usually suppose that all the problems to be solved ment over exhaustive search. The comparator gives us are iniitially well defined [1]. By this we meain that with information about partial success, to be sure. But we each problem we are given somie systematic way to need also some way of using this information to direct decide when a proposed solution is acceptable. Most of the pattern of search in promising directionis; to select the experimental work discussed here is concerined with new trial points which are in some senise "like," or such well-definied problems as are met in theorem-prov- "similar to," or "in the same direction as" those which ing, or in games with precise rules for play and scoring.

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