Machine Intelligence 7 - AITopics

More documents

Recommendations

Info

MACHINE INTELLIGENCE 7 edited by BERNARD MELTZER Department of Computational Logic University of Edinburgh and DONALD MICHIE Department of <strong>Machine</strong> <strong>Intelligence</strong> University of Edinburgh with a preface by LORD BOWDEN EDINBURGH at the University Press
Page 1 and 2: 114; tt.t. 1 s
Page 3: MACHINE INTELLIGENCE 7
Page 7 and 8: PREFACE 'Machine Intelligence' has
Page 9 and 10: PREFACE puters available to ordinar
Page 11: PREFACE The concept of gestalt is h
Page 14 and 15: CONTENTS PROBLEM-SOLVING AUTOMATA 2
Page 16 and 17: INTRODUCTION that the desirability
Page 19 and 20: 1 On Alan Turing and the Origins of
Page 21 and 22: RANDELL From Dr Herman H. Goldstine
Page 23 and 24: RANDELL propose a changeable progra
Page 25 and 26: RANDELL been sworn to secrecy. Thus
Page 27 and 28: RANDELL because of the enhanced pos
Page 29 and 30: RANDELL to the extent of providing
Page 31 and 32: RANDELL ley, I may be wrong . . . D
Page 33 and 34: RANDELL gram computer to operate wa
Page 35 and 36: RAND ELL Phillips, E.W. (1965b) Pre
Page 37: PROGRAM PROOF AND MANIPULATION
Page 40 and 41: PROGRAM PROOF AND MANIPULATION x: =
Page 42 and 43: PROGRAM PROOF AND MANIPULATION Si I
Page 44 and 45: PROGRAM PROOF AND MANIPULATION (c)
Page 46 and 47: PROGRAM PROOF AND MANIPULATION We d
Page 48 and 49: PROGRAM PROOF AND MANIPULATION By w
Page 50 and 51: PROGRAM PROOF AND MANIPULATION Figu
Page 52 and 53: PROGRAM PROOF AND MANIPULATION Cons
Page 54 and 55:
PROGRAM PROOF AND MANIPULATION m=CO
Page 56 and 57:
• • PROGRAM PROOF AND MANIPULAT
Page 58 and 59:
PROGRAM PROOF AND MANIPULATION also
Page 60 and 61:
PROGRAM PROOF AND MANIPULATION (Tit
Page 62 and 63:
PROGRAM PROOF AND MANIPULATION j=RE
Page 64 and 65:
PROGRAM PROOF AND MANIPULATION Figu
Page 66 and 67:
PROGRAM PROOF AND MANIPULATION The
Page 68 and 69:
PROGRAM PROOF AND MANIPULATION comp
Page 70 and 71:
PROGRAM PROOF AND MANIPULATION •
Page 72 and 73:
PROGRAM PROOF AND MANIPULATION find
Page 74 and 75:
PROGRAM PROOF AND MANIPULATION wher
Page 76 and 77:
PROGRAM PROOF AND MANIPULATION TCMP
Page 78 and 79:
PROGRAM PROOF AND MANIPULATION (G1.
Page 80 and 81:
PROGRAM PROOF AND MANIPULATION Diff
Page 82 and 83:
PROGRAM PROOF AND MANIPULATION APPE
Page 84 and 85:
PROGRAM PROOF AND MANIPULATION CA 0
Page 86 and 87:
PROGRAM PROOF AND MANIPULATION IIII
Page 89 and 90:
4 Building-in Equational Theories G
Page 91 and 92:
PLOTKIN be carried through using th
Page 93 and 94:
PLOTKIN constants — the numerals
Page 95 and 96:
PLOTKIN We use the equality symbol
Page 97 and 98:
PLOTKIN If /(S)> 0, there is a lite
Page 99 and 100:
PLOTKIN from Var (82) u V and for e
Page 101 and 102:
and terms ti, ui (i=1, n), t, u, v'
Page 103 and 104:
PLOTKIN not checked, one can obtain
Page 105 and 106:
PLOTKIN infinite set of refutations
Page 107 and 108:
5 Theorem Proving in Arithmetic wit
Page 109 and 110:
COOPER Step 3 Simplify each relatio
Page 111 and 112:
COOPER but F(x0) is true, then at l
Page 113 and 114:
COOPER where R1, . R. are arithmeti
Page 115:
COOPER amples with the same relatio
Page 118 and 119:
COMPUTATIONAL LOGIC the resolvent R
Page 120 and 121:
COMPUTATIONAL LOGIC The value of th
Page 122 and 123:
COMPUTATIONAL LOGIC The value of TE
Page 124 and 125:
COMPUTATIONAL LOGIC (z, 7 . (f x y)
Page 126 and 127:
COMPUTATIONAL LOGIC GETLIT(CL,K). G
Page 128 and 129:
01001 IVNOIIVII1dPIO3 c4 c6 c3 ((+(
Page 130 and 131:
COMPUTATIONAL LOGIC the figure exhi
Page 132 and 133:
COMPUTATIONAL LOGIC structure shari
Page 134 and 135:
COMPUTATIONAL LOGIC A clause is a H
Page 136 and 137:
COMPUTATIONAL LOGIC resolution have
Page 138 and 139:
COMPUTATIONAL LOGIC It should be no
Page 140 and 141:
• COMPUTATIONAL LOGIC Since both
Page 142 and 143:
COMPUTATIONAL LOGIC assumption, m +
Page 144 and 145:
COMPUTATIONAL LOGIC generated by th
Page 146 and 147:
COMPUTATIONAL LOGIC are two (not ne
Page 148 and 149:
COMPUTATIONAL LOGIC Since these two
Page 150 and 151:
COMPUTATIONAL LOGIC Figure 1. Loop
Page 152 and 153:
COMPUTATIONAL LOGIC talking about u
Page 155:
INFERENTIAL AND HEURISTIC SEARCH
Page 158 and 159:
INFERENTIAL AND HEURISTIC SEARCH de
Page 160 and 161:
INFERENTIAL AND HEURISTIC SEARCH mu
Page 162 and 163:
INFERENTIAL AND HEURISTIC SEARCH by
Page 164 and 165:
INFERENTIAL AND HEURISTIC SEARCH ti
Page 166 and 167:
INFERENTIAL AND HEURISTIC SEARCH co
Page 168 and 169:
INFERENTIAL AND HEURISTIC SEARCH co
Page 170 and 171:
C) o o C) ri\41 141‘ • • —
Page 172 and 173:
INFERENTIAL AND HEURISTIC SEARCH ne
Page 174 and 175:
INFERENTIAL AND HEURISTIC SEARCH Mo
Page 176 and 177:
INFERENTIAL AND HEURISTIC SEARCH Ta
Page 178 and 179:
INFERENTIAL AND HEURISTIC SEARCH Ac
Page 180 and 181:
INFERENTIAL AND HEURISTIC SEARCH Q
Page 183 and 184:
10 And-or Graphs, Theorem-proving G
Page 185 and 186:
KOWALSKI specify a search space of
Page 187 and 188:
KOWALSKI sharing a single copy of a
Page 189 and 190:
KOWALSKI A derivation D is a finite
Page 191 and 192:
KOWALSKI goals to be solved simulta
Page 193 and 194:
KOWALSKI to select and generate can
Page 195 and 196:
KOWALSKI Figure 5. The search space
Page 197 and 198:
KOWALSKI (1) size, the number of se
Page 199 and 200:
KOWALSKI =h. A diagonal search stra
Page 201 and 202:
KOWALSKI Theorem do not specify wha
Page 203 and 204:
KOWALSKI solution path whose additi
Page 205 and 206:
KOWALSKI carry no indication of how
Page 207 and 208:
KOWALSKI within which unit preferen
Page 209 and 210:
KOWALSKI municating special-purpose
Page 211 and 212:
11 An Approach to the Frame Problem
Page 213 and 214:
SAND EWALL principle be re-proved f
Page 215 and 216:
SANDEWALL used in the reasoning pro
Page 217 and 218:
SANDEWALL A Unless B and if we wish
Page 219 and 220:
SANDEWALL during the planning phase
Page 221 and 222:
12 A Heuristic Solution to the Tang
Page 223 and 224:
DEUTSCII AND HAYES puzzles is Read'
Page 225 and 226:
DEUTSCH AND HAYES application of al
Page 227 and 228:
DEUTSCH AND HAYE quite early on tha
Page 229 and 230:
DEUTSCH AND HAYES subpuzzles do not
Page 231 and 232:
DEUTSCII AND IIAYES vertices (or no
Page 233 and 234:
• • DEUTSCH AND HAYES • • .
Page 235 and 236:
DEUTSCH AND HAYES 2 2 2 2 2 • III
Page 237 and 238:
DEUTSCH AND HAYES pieces. Puzzle pi
Page 239 and 240:
DEUTSCH AND HAYES fully described b
Page 241 and 242:
DEUTSCH AND HAYES be fitted into ar
Page 243 and 244:
(2) ciwool] S3s111a.03 1 2110.0102
Page 245 and 246:
DEUTSCH AND HAYES 4.9 Puzzle pieces
Page 247 and 248:
DEUTSCH AND IIAYES r-> direct-match
Page 249 and 250:
DEUTSCII AND HAYES The rules of low
Page 251 and 252:
• .• DEUTSCH AND HAYES It will
Page 253 and 254:
DEUTSCH AND HAYES X .---- x+ I dire
Page 255 and 256:
DEUTSCII AND HAYES •• • •
Page 257 and 258:
13 A Man-Machine Approach for Creat
Page 259 and 260:
KROLAK AND NELSON those who exhibit
Page 261 and 262:
KROLAK AND NELSON normally we expec
Page 263 and 264:
KROLAK AND NELSON tried to capture
Page 265 and 266:
KROLAK AND NELSON V.S.P. 1967) on r
Page 267 and 268:
KROLAK AND NELSON generated overlay
Page 269 and 270:
KROLAK AND NELSON allowed to be zer
Page 271 and 272:
KROLAK AND NELSON Table 1. Loads an
Page 273 and 274:
▪ ts.) Schoo▪ • , •) indica
Page 275 and 276:
Figure 3. Modified initial estimate
Page 277 and 278:
t.) 1 I ■ 1 -- to School 1 --- to
Page 279 and 280:
KROLAK AND NELSON Cost =7301.62 o o
Page 281 and 282:
KROLAK AND NELSON Collins, J.S. (19
Page 283 and 284:
14 Heuristic Theory Formation: Data
Page 285 and 286:
BUCHANAN, FEIGENBAUM AND SRIDHARAN
Page 287 and 288:
BUCHANAN, FEIGENBAUM AND SRIDIIARAN
Page 289 and 290:
Page 291 and 292:
Page 293 and 294:
Page 295 and 296:
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307:
PERCEPTUAL AND LINGUISTIC MODELS
Page 310 and 311:
PERCEPTUAL AND LINGUISTIC MODELS An
Page 312 and 313:
PERCEPTUAL AND LINGUISTIC MODELS Re
Page 314 and 315:
PERCEPTUAL AND LINGUISTIC MODELS al
Page 316 and 317:
PERCEPTUAL AND LINGUISTIC MODELS fo
Page 318 and 319:
PERCEPTUAL AND LINGUISTIC MODELS RE
Page 320 and 321:
PERCEPTUAL AND LINGUISTIC MODELS "T
Page 322 and 323:
PERCEPTUAL AND LINGUISTIC MODELS TR
Page 324 and 325:
PERCEPTUAL AND LINGUISTIC MODELS no
Page 326 and 327:
PERCEPTUAL AND LINGUISTIC MODELS Pr
Page 328 and 329:
PERCEPTUAL AND LINGUISTIC MODELS de
Page 330 and 331:
PERCEPTUAL AND LINGUISTIC MODELS di
Page 332 and 333:
PERCEPTUAL AND LINGUISTIC MODELS it
Page 334 and 335:
PERCEPTUAL AND LINGUISTIC MODELS (0
Page 336 and 337:
PERCEPTUAL AND LINGUISTIC MODELS 9a
Page 338 and 339:
PERCEPTUAL AND LINGUISTIC MODELS fr
Page 340 and 341:
PERCEPTUAL AND LINGUISTIC MODELS in
Page 342 and 343:
PERCEPTUAL AND LINGUISTIC MODELS ex
Page 344 and 345:
PERCEPTUAL AND LINGUISTIC MODELS Th
Page 346 and 347:
PERCEPTUAL AND LINGUISTIC MODELS di
Page 348 and 349:
PERCEPTUAL AND LINGUISTIC MODELS My
Page 350 and 351:
PERCEPTUAL AND LINGUISTIC MODELS BR
Page 352 and 353:
PERCEPTUAL AND LINGUISTIC MODELS se
Page 354 and 355:
PERCEPTUAL AND LINGUISTIC MODELS I
Page 357 and 358:
18 The Syntactic Inference Problem
Page 359 and 360:
HERMAN AND WALKER 1971a,b). In this
Page 361 and 362:
HERMAN AND WALKER For any DpxL-sche
Page 363 and 364:
HERMAN AND WALKER symbols from Go o
Page 365 and 366:
HERMAN AND WALKER facts arise which
Page 367 and 368:
HERMAN AND WALKER of strings which
Page 369 and 370:
HERMAN AND WALKER We shall demonstr
Page 371 and 372:
HERMAN AND WALKER unbounded growth
Page 373 and 374:
19 Parallel and Serial Methods of P
Page 375 and 376:
W1LLSHAW AND BUNEMAN Each pattern i
Page 377 and 378:
WILLSHAW AND BUNEMAN this means tha
Page 379 and 380:
WILLSHAW AND BUNEMAN 4. GRAPHICAL R
Page 381 and 382:
WILLSHAW AND BUNEMAN (3) Given a se
Page 383:
WILLSHAW AND BUNEMAN taking the for
Page 386 and 387:
PERCEPTUAL AND LINGUISTIC MODELS be
Page 388 and 389:
PERCEPTUAL AND LINGUISTIC MODELS (a
Page 390 and 391:
PERCEPTUAL AND LINGUISTIC MODELS R=
Page 392 and 393:
PERCEPTUAL AND LINGUISTIC MODELS Us
Page 394 and 395:
PERCEPTUAL AND LINGUISTIC MODELS co
Page 396 and 397:
PERCEPTUAL AND LINGUISTIC MODELS on
Page 398 and 399:
PERCEPTUAL AND LINGUISTIC MODELS wi
Page 400 and 401:
PERCEPTUAL AND LINGUISTIC MODELS Fi
Page 403:
PROBLEM-SOLVING AUTOMATA
Page 406 and 407:
PROBLEM-SOLVING AUTOMATA drawn from
Page 408 and 409:
PROBLEM-SOLVING AUTOMATA vi(fZ, .,
Page 410 and 411:
PROBLEM-SOLVING AUTOMATA with the d
Page 412 and 413:
PROBLEM-SOLVING AUTOMATA Consider t
Page 414 and 415:
PROBLEM-SOLVING AUTOMATA of the equ
Page 416 and 417:
PROBLEM-SOLVING AUTOMATA estimate t
Page 418 and 419:
PROBLEM-SOLVING AUTOMATA automata--
Page 420 and 421:
PROBLEM-SOLVING AUTOMATA automata -
Page 423 and 424:
23 Some New Directions in Robot Pro
Page 425 and 426:
FIKES, HART AND NILSSON to be achie
Page 427 and 428:
FIKES, HART AND NILSSON There are s
Page 429 and 430:
FIKES, HART AND NILSSON (2) The rea
Page 431 and 432:
FIKES, HART AND NILSSON To progress
Page 433 and 434:
FIKES, HART AND NILSSON actions of
Page 435 and 436:
FIKES, HART AND NILSSON posed by a
Page 437 and 438:
FIKES, HART AND NILSSON indicates t
Page 439 and 440:
FIKES, HART AND NILSSON CHECKDOOR (
Page 441 and 442:
FIKES, HART AND NILSSON To make the
Page 443 and 444:
FIKES, HART AND NILSSON team robot
Page 445 and 446:
FIKES, HART AND NILSSON posed in pr
Page 447 and 448:
FIKES, HART AND NILSSON definer is
Page 449 and 450:
24 The MIT Robot P. H. Winston Arti
Page 451 and 452:
WINsTON ARROW FORK PSI Figure 2. Th
Page 453 and 454:
WINSTON Figure 6. The fork vertex c
Page 455 and 456:
WINSTON time. The issue was avoided
Page 457 and 458:
WINSTON Given this fact a simple se
Page 459 and 460:
WINSTON less ambiguity than does a
Page 461 and 462:
WINSTON Each action module is charg
Page 463 and 464:
WINSTON mysterious at first. But, l
Page 465 and 466:
WINSTON alternative of using clues
Page 467 and 468:
WINSTON generates in the machine an
Page 469 and 470:
WINSTON objects and the pointers re
Page 471 and 472:
WINSTON current implementation, dif
Page 473 and 474:
WINSTON better by devising a little
Page 475 and 476:
WINSTON 2. A large network of progr
Page 477 and 478:
WINSTON FIND-ALTITUDE. This determi
Page 479 and 480:
FIND-DIMENSIONS WINSTON MANIPULATE
Page 481:
WINSTON Hewitt, C. (1972) Descripti
Page 484 and 485:
PROBLEM-SOLVING AUTOMATA the system
Page 486 and 487:
PROBLEM-SOLVING AUTOMATA Figure I I
Page 488 and 489:
PROBLEM-SOLVING AUTOMATA resolution
Page 490 and 491:
PROBLEM-SOLVING AUTOMATA Unit A pla
Page 492 and 493:
PROBLEM-SOLVING AUTOMATA rotate abo
Page 494 and 495:
PROBLEM-SOLVING AUTOMATA HREG(N); R
Page 496 and 497:
PROBLEM-SOLVING AUTOMATA • DISPLA
Page 498 and 499:
PROBLEM-SOLVING AUTOMATA the ii316
Page 500 and 501:
INDEX Deutsch 205-40 grammar 293-5,
Page 502 and 503:
• INDEX Pavlidis 307, 323 Paz 352
show all

Machine Intelligence 7 - AITopics

Create successful ePaper yourself

Delete template?

Save as template?