EAcknowledgments I A. M. Turing, Computing machinery and intelhgence, by permlsslon from the editors of Mid, October, 1950, 59:433-460 A. Newell, J. C Shaw, and H Slmon, Chess playing programs and the problem of complexity, by permission from the IBM Journal of Re- A L Samuel, Some studres m machine Iearmng usmg the game of checkers, by permission from the IBM Journal of Research and Development, t search and Development, October, 1958, 2:320-335. 1 July, 1959, 3:211-229 The author acknowledges: I Many drfferent people have contrrbuted to these studies throrrglz stimulatrng drscussions of the basic problems. From time to time the writer was assisted by several dzfferent programmers, although most of the detailed work was hu own. The forbearance of the machrne room operators and their willingness to play the machlne at all hours of the day and night are also greatly appreciated. A. Newell, J. C. Shaw, and H. Simon, Empirical explorations with the Loglc Theory Machine, by permission of the authors from the Pto- ceedings of the Western Joint Computer Conference, 1957, 15:218- 239. This research was part of a project conducted jomtly by NewclI the Carnegie Institute of Technology. I and Shaw of the RAND Corporatlon, Santa Monm, and H Smon of , H. Gelernter, Reahzatlon of a geometry-theorem provmg machine, by I permission from the Proceedrngs of an International Conference on In- formation Processmg, Pans: UNESCO House, 1959, pp. 273-282 The author acknowledges : The technical and programming assistance of J X. Ransen and D. W Loveland has been indrspensable to the success of thu prolect N Rochester and J. McCarthy contributed much to the early dcvel- opment of ideas, and Rochester supplied the necessary ndmlnrstrntlve support as well Other members of the Information Research Depart- ment of IBM, and W G Bouricius, P. C Grlmore, J. P. Laznrus, and P. D. Welch, ~n partrcular, contributed to the author’s under- standmng of the problem m his conversations with them The research project itself is a consequence of the Dartmouth Summer Research Project on Artificial Intelbgence held rn 1956, during which M. L. Minsky pornted out the potential utility of fhe dragram to a geometry theorem-proving machine. ix70 ARTIFICIAL INTELLIGENCE White is lost but relatively best was 22. R-Q3 blockadlng the passed Queen Pawn 22 R-QBI wdrcates that an order is mrssmg to nvmd exchanges after losmg mnterial, unless such ewhanges deserve a high rutlng for spec& reasons covered by other orders. 22 ... KR-QB 1 23 QR-Q1 White IS lust floundermg in a lost posltlon 23 ... KR-B 6 24 P-N4 “There are no good moves In bad positions!” 24 ... KRXP 25 B-N3 Best, White at least stops the mating attack. 25 ... P-Q6 26 R-QB1 B-N4 26 R-QBl indicates that an order IS mlsstng that would make the ma- chrne avold getting forked. Better was 26 . . . , P-Q7 winnmg instantly (26 . . , P-Q7, 27. R X R, P X R = Qch, 28 K-N2, Q-Q8!, 29. R-B8ch, B-Ql). 27 RXR PXR 29 RXQ BXR 30 Resigns 28 B-K5 P-B8 Q Best, but 1’111 sure the programmers were just gettmg tlrcdl Sldt test games give mdeed excellent mdlcrrtions as to the type of genpr(1l prrncrples the program should include in nddltmt to tnuterrul Dulance, de- velopment, and center control, to elrmlnate untlposrtionul moves as much as posszble. SOME STUDIES IN MACHINE LEARNING USING THE GAME OF CHECKERS by A L. Samuel Introduction The studies reported here have been concerned with the programming of a dlgital computer to behave in a way whlch, if done by human beings or animals, would be described as mvolvmg the process of learning. Whlle thls is not the place to dwell on the importance of machine-learnmg pro- cedures, or to discourse on the philosophical aspectsY1 there is obviously J. very large amount of work, now done by people, which IS qulte trlvial In Its dcmands on the lntcllect but does, nevertheless, involve somc lcarning We have at our command computers wlth adequate data-lxmdllng abllity and wlth sufficlent computatlonal speed to make use of machlne-learn~ng technlques, but our knowledge of the baslc prmciples of these tcchnlqucs I$ still rudlmcntary Lackmg such knowledge, it IS nccessaIy to spccrfy rncthods of problem solution in minute and exact dctail, a timc-consuming and costly procedure, Programmmg computers to learn from expcrlcnce should eventually eliminate the need for much of thls detailed progtam- ming effort. General Methods of Appronch At the outset it mlght be well to distinguish sharply between two genelal approaches to the problem of machme learnmg. One method, which nllght be called the Neural-Net Appronclz, deals with the possibility of induclng learned behavior into a randomly connected switching net (or its smula- Some of these are qulte profound and have a bearlng on the quesllons ralsed by Nelson Goodman m Fact, Frction and Forecasr, Carnbndge, Mass : Warvard, 1954 7172 ARTIFICIAL INTELLIGENCE tion on a dlgital computer) as a result of a reward-and-punishment routme A second, and much more efficient approach, is to produce the equlvalent of a hlghly organized network whdl has been deslgned to learn only cer- tain specific thrngs The first method should lead to the development of general-purpose lcarnrng machmes A comparison between the size of the swltchlng nets that can be reanonably constructed or simulated at the pres- ent time and the s~ze of the neural nets used by anlmals, suggests that wc have a long way to go before we obtain practml devlces The second procedure requlres reprogrammmg for each new application, but it is capable of reahzation at the present tlme The experlments to be descrlbed here were based on thls second approach. Choice of Problem For some years the wnter has devoted 111s spare tlme to the subject of ma- chine learning and has concentrated on the development of learnmg pro- cedures as applied to games s A game provides a convenient vehicle for such study as contrasted with a problem taken from life, since many of the complmtions of detail are removed Checkers, rather than chess (Shannon, 1950, Bernstem and Roberts, 1958b; Klster et al, 1957; Newell,
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