Machine Intelligence 7 - AITopics

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CONTENTS PROBLEM-SOLVING AUTOMATA 22 Some effects in the collective behaviour of automata. V.I.VARSHAVSKY 389 23 Some new directions in robot problem solving. R.E.FIREs, P.E.HART and N.J.NiLssoN 405 24 The MIT robot. P.H.WiNsToN 431 25 The Mark 1.5 Edinburgh robot facility. 465 H. G.BARROW and G.F.CRAWFORD INDEX 481 xii
INTRODUCTION Among the many properties ascribed to the magical number seven is that of marking out the significant epochs of a human life-span — seven years from birth to departure from the kindergarten, another seven to puberty, another seven to majority. The occasion of the Seventh International Machine Intelligence Workshop is perhaps an appropriate moment to take stock. Views differ as to exactly which of the successive thresholds is the one on which machine intelligence research is now poised. But there is no mistaking the sense of transition, felt both by its practitioners, who claim that their field is at last attaining maturity, and in a rather different way by its interested Spectators. The latter rightly point out that if maturation brings opportunity and new powers it also brings the obligation to earn a living. In an invited Address of bracing candour, Lord Bowden called on our profession to examine itself with this criterion in mind. We reproduce his Address as the Preface to this volume. The seventh Workshop was also marked by a new and unexpected addition to the study of origins. Volume 5 contained A. M.Turing's previously unpublished essay 'Intelligent Machinery'; we here make available a recent work of scholarship by Professor Brian Randell which has cast entirely new light on the technical milieu in which Turing first arrived at his vision of intelligence in machines. In the course of his pertinacious study, Professor Randell approached the Prime Minister for access to papers still held under security classification. Although the request was not granted, Mr Heath and his advisers did not let the matter rest. In due course Professor Randell was informed that the Prime Minister had decided that an official record of the early electronic computer developed during the Second World War Should be compiled in consultation with those who were concerned in the Project. It will surely be a source of widespread gratification that Professor Randell's initiative has catalysed action to preserve the technical facts for the record. Mention was made earlier of a sense of transition. Not only is there a new awareness of the obligation to earn a living, but there is an even keener awareness that this is far from being the whole point. A man may design a Program to carry out a particular cognitive activity with the idea of making it as efficient as possible at this task; or another man may write a program to carry out the same cognitive task, with the idea that it may be suggestive for understanding the way human beings carry out that task. It is important xi ii
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Page 3: MACHINE INTELLIGENCE 7
Page 6 and 7: © 1972 Edinburgh University Press
Page 8 and 9: PREFACE the speed of calculation is
Page 10 and 11: PREFACE them on the shelves until t
Page 13: CONTENTS INTRODUCTION xiii PREHISTO
Page 17: PREHISTORY
Page 20 and 21: PREHISTORY discussion of the later
Page 22 and 23: PREHISTORY beginning to wane. I eve
Page 24 and 25: PREHISTORY I don't think that I can
Page 26 and 27: PREHISTORY discouraged by the slown
Page 28 and 29: PREHISTORY carried some fixed patte
Page 30 and 31: PREHISTORY After completing actuari
Page 32 and 33: PREHISTORY modified program cards,
Page 34 and 35: PREHISTORY REFERENCES Champemowne,
Page 36 and 37: PREHISTORY and they were joined in
Page 39 and 40: Some Techniques for Proving Correct
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Page 43 and 44: IIURSTALL a'(0> 0. The distinction
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Page 47 and 48: BURSTALL • j =RE VER S E(k) Assum
Page 49 and 50: BURSTALL j=REVERSE(k) Assume rev: (
Page 51 and 52: • BURSTALL Assume n, Ai,.. ., An,
Page 53 and 54: BURSTALL (iii) If E is 11E1 and D c
Page 55 and 56: BURSTALL j=CYCLICREVERSE(1) (*(/->n
Page 57 and 58: BURSTALL Our previous discussion de
Page 59 and 60: BURSTALL Taking the case where f2 c
Page 61 and 62: 13URSTALL In the case of cons and a
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BURSTALL Eilenberg, S. & Wright, J.
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3 Proving Compiler Correctness in a
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MILNER AND WEYHRAUCII and q, else r
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MILNER AND WEYHRAUCH We use `8e for
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I MILNER AND WEYHRAUCH ((JF&(count(
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MILNER AND WEYIIRAUCH (iii) For eac
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MILNER AND WEYHRAUCII We have abbre
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MILNER AND WEYHRAUCH give us a feel
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MILNER AND WEYIIRAUCII APPENDIX 1:
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.71 APPENDIX 3: proof of the McCart
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HDIIVIEHA3M CENIV 11UNIITAI I 1 1 1
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COMPUTATIONAL LOGIC
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COMPUTATIONAL LOGIC N((±)P(ti, t
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COMPUTATIONAL LOGIC (3) Find the di
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COMPUTATIONAL LOGIC are number term
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COMPUTATIONAL LOGIC A set of litera
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COMPUTATIONAL LOGIC Conditions 4 an
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COMPUTATIONAL LOGIC In our opinion,
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COMPUTATIONAL LOGIC Theorem 3. If S
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COMPUTATIONAL LOGIC and V. and furt
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COMPUTATIONAL LOGIC Kowalski, R.K.
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COMPUTATIONAL LOGIC another. It is
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COMPUTATIONAL LOGIC how the effect
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COMPUTATIONAL LOGIC The enlarged al
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COMPUTATIONAL LOGIC being within Pr
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6 The Sharing of Structure in Theor
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BOYER AND MOORE of a substitution S
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BoYER AND MOORE Otherwise TERM is n
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BOYER AND MOORE binding environment
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BOYER AND MOORE TERMB,INDEXB in the
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BOYER AND MOORE set INDEXB to INDEX
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H110011 CINV /13A0£1 21 .V, - cs'
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BOYER AND MOORE UNIFY can be made t
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7 Some Special Purpose Resolution S
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KUEHNER resolvent in D. Let C1 and
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KUEHNER obtained by deleting the gi
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KUEHNER D be any sPu-refutation of
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KUEHNER BI-DIRECTIONAL SPU/SNL RESO
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KUEHNER of finding a refutation of
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8 Deductive Plan Formation in Highe
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DARLINGTON and `c3' are set variabl
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DARLINGTON submitted to our theorem
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DARLINGTON Some further problems wh
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DARLINGTON Hewitt, C. (1971). Proce
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9 G-deduction D. Michie, R. Ross an
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MICIIIE, ROSS AND SHANNAN Search oc
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MICHIE, ROSS AND SHANNAN root node
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MICHIE, ROSS AND SHANNAN Positive c
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MICHIE, ROSS AND SHANNAN mere insta
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MICHIE, ROSS AND SHANNAN G-deduct i
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MICHIE, ROSS AND SHANNAN (4) additi
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MICHIE, ROSS AND SHANNAN (4) repres
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MICHIE, ROSS AND SHANNAN completene
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MICHIE, ROSS AND SHANNAN RESULTS Th
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MICHIE, ROSS AND SIIANNAN and, more
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• MICHIE, ROSS AND SHANNAN DM {{P
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S a Skolem constant T THERE Variabl
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INFERENTIAL AND HEURISTIC SEARCH se
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INFERENTIAL AND HEURISTIC SEARCH 0
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INFERENTIAL AND HEURISTIC SEARCH in
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INFERENTIAL AND HEURISTIC SEARCH no
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INFERENTIAL AND HEURISTIC SEARCH Re
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INFERENTIAL AND HEURISTIC SEARCH th
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INFERENTIAL AND HEURISTIC SEARCH be
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INFERENTIAL AND HEURISTIC SEARCH h
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INFERENTIAL AND HEURISTIC SEARCH so
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INFERENTIAL AND HEURISTIC SEARCH BI
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INFERENTIAL AND HEURISTIC SEARCH FI
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INFERENTIAL AND HEURISTIC SEARCH Em
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INFERENTIAL AND HEURISTIC SEARCH ob
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INFERENTIAL AND HEURISTIC SEARCH Co
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INFERENTIAL AND HEURISTIC SEARCH mo
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INFERENTIAL AND HEURISTIC SEARCH EP
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INFERENTIAL AND HEURISTIC SEARCH TH
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INFERENTIAL AND HEURISTIC SEARCH Sa
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INFERENTIAL AND HEURISTIC SEARCH no
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INFERENTIAL AND HEURISTIC SEARCH (a
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• • 0 • • • KO • •
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INFERENTIAL AND HEURISTIC SEARCH an
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• INFERENTIAL AND HEURISTIC SEARC
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INFERENTIAL AND HEURISTIC SEARCH (a
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• INFERENTIAL AND HEURISTIC SEARC
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INFERENTIAL AND HEURISTIC SEARCH °
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—AO INFERENTIAL AND HEURISTIC SEA
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• • • A • • 0 • • •
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INFERENTIAL AND HEURISTIC SEARCH pr
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• • • • • INFERENTIAL AND
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INFERENTIAL AND HEURISTIC SEARCH di
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,0* INFERENTIAL AND HEURISTIC SEARC
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INFERENTIAL AND HEURISTIC SEARCH cr
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INFERENTIAL AND HEURISTIC SEARCH ex
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INFERENTIAL AND HEURISTIC SEARCH 2.
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INFERENTIAL AND HEURISTIC SEARCH CO
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INrERENTIAL AND HEURISTIC SEARCH Ta
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• 0.00 1.00 2.00 3.00 4.00 Figure
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• to School 1 hool ,----- \ 4,% 3
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INFERENTIAL AND HEURISTIC SEARCH Ob
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INFERENTIAL AND HEURISTIC SEARCH Sp
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INFERENTIAL AND HEURISTIC SEARCH (i
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INFERENTIAL AND HEURISTIC SEARCH Ta
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INFERENTIAL AND HEURISTIC SEARCH Ta
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INFERENTIAL AND HEURISTIC SEARCH an
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INFERENTIAL AND IIEURISTIC SEARCH B
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INFERENTIAL AND HEURISTIC SEARCH Co
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INFERENTIAL AND HEURISTIC SEARCH of
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15 Mathematical and Computational M
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FRIEDMAN from base tree to surface
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FRIEDMAN Who did Jack find out that
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FRIEDMAN only. All computer runs we
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FRIEDMAN control program can be wri
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FRIEDMAN APPENDIX A "COMPUTER EXPER
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FRIEDMAN APPENDIX B "COMPUTER EXPER
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16 Web Automata and Web Grammars A.
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ROSENFELD AND MILGRAM (4) co' is ob
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ROSENFELD AND MILGRAM is applied, p
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ROSENFELD AND MILGRAM 7 I'S 0(Y, Y)
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ROSENFELD AND MILGRAM (5) If the ab
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ROSENFELD AND MILGRAM 3.4 Web gramm
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ROSENFELD AND MILGRAM where (11) ap
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ROSENFELD AND MILGRAM (d) Nog={ml,m
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ROSENFELD AND MILGRAM out that the
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17 Utterances as Programs D. J. M.
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DAVIES AND ISARD complex situation
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DAVIES AND ISARD and PLANNER (Hewit
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DAVIES AND ISARD which are consider
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DAVIES AND ISARD questions, and the
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DAVIES AND ISARD To help clarify ou
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DAVIES AND ISARD numbername(term(un
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DAVIES AND ISARD and a command, we
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PERCEPTUAL AND LINGUISTIC MODELS sl
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PERCEPTUAL AND LINGUISTIC MODELS 2.
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PERCEPTUAL AND LINGUISTIC MODELS an
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PERCEPTUAL AND LINGUISTIC MODELS 4.
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PERCEPTUAL AND LINGUISTIC MODELS (f
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PERCEPTUAL AND LINGUISTIC MODELS It
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PERCEPTUAL AND LINGUISTIC MODELS Le
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PERCEPTUAL AND LINGUISTIC MODELS He
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PERCEPTUAL AND LINGUISTIC MODELS ha
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PERCEPTUAL AND LINGUISTIC MODELS Th
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PERCEPTUAL AND LINGUISTIC MODELS wh
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PERCEPTUAL AND LINGUISTIC MODELS jo
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PERCEPTUAL AND LINGUISTIC MODELS Fi
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20 Approximate Error Bounds in Patt
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ITO (1) Proof of Pe ‹. Q„ We co
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ITO Q0=1-11 p(x)[p(ci I x)- p(c21x)
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ITO Thus we have proved eq. (49). U
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21 A Look at Biological and Machine
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GREGORY logical operations than are
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GREGORY no created contours, althou
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GREGORY black and the white illusor
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GREGORY appear trivial. There is no
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22 Some Effects in the Collective B
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VARSHAVSKY The set of automorphisms
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VARSHAVSKY that after each play of
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VARSHAVSKY do not use recessive str
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VARSHAVSKY M,\ M" .... - 397 Figure
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VARSHAVSKY Figure 2 I automata-- 1
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Forming the balance equation, we ha
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VARSHAYSKY This obviously suggests
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VARSHAVSKY This obviously suggests
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PROBLEM-SOLVING AUTOMATA be related
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, PROBLEM-SOLVING AUTOMATA either t
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PROBLEM-SOLVING AUTOMATA room; thus
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PROBLEM-SOLVING AUTOMATA Execution
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PROBLEM-SOLVING AUTOMATA as PLANNER
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PROBLEM-SOLVING AUTOMATA Table I. E
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PROBLEM-SOLVING AUTOMATA probably b
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PROBLEM-SOLVING AUTOMATA expanded w
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PROBLEM-SOLVING AUTOMATA extent tha
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PROBLEM-SOLVING AUTOMATA actions of
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PROBLEM-SOLVING AUTOMATA The algori
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PROBLEM-SOLVING AUTOMATA interest.
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PROBLEM-SOLVING AUTOMATA be that AI
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PROBLEM-SOLVING AUTOMATA Finally a
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PROBLEM-SOLVING AUTOMATA advantage.
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PROBLEM-SOLVING AUTOMATA Why should
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PROBLEM-SOLVING AUTOMATA A line in
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PROBLEM-SOLVING AUTOMATA places whe
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PROBLEM-SOLVING AUTOMATA scene. But
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PROBLEM-SOLVING AUTOMATA 4. The sys
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PROBLEM-SOLVING AUTOMATA Let me see
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PROBLEM-SOLVING AUTOMATA LEARNING T
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PROBLEM-SOLVING AUTOMATA pedestal n
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PROBLEM-SOLVING AUTOMATA relationsh
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PROBLEM-SOLVING AUTOMATA relations.
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PROBLEM-SOLVING AUTOMATA program bo
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PROBLEM-SOLVING AUTOMATA MOVE. To m
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PROBLEM-SOLVING AUTOMATA CHOOSE-TO-
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PROBLEM-SOLVING AUTOMATA Moving in
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25 The Mark 1.5 Edinburgh Robot Fac
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BARROW AND CRAWFORD or more element
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BARROW AND CRAWFORD reduce this to
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BARROW AND CRAWFORD SATELLITE COMPU
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BARROW AND CRAWFORD DATA READY ACCE
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BARROW AND CRAWFORD or contents alt
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movETo(x, y); BARROW AND CRAWFORD p
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BARROW AND CRAWFORD are performed b
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INDEX Abe 307, 323 ACE 3, 4, 6, 10,
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Jensen 78, 132 Johnson 245 Jordan 1
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Steinberg 247 Stephenson 367 Strach
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