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- Page 8 and 9: PREFACE the speed of calculation is
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- Page 13 and 14: CONTENTS INTRODUCTION xiii PREHISTO
- Page 15 and 16: INTRODUCTION Among the many propert
- Page 17: PREHISTORY
- 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
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- 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
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- Page 42 and 43: PROGRAM PROOF AND MANIPULATION Si I
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PROGRAM PROOF AND MANIPULATION comp
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PROGRAM PROOF AND MANIPULATION •
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PROGRAM PROOF AND MANIPULATION find
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PROGRAM PROOF AND MANIPULATION wher
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PROGRAM PROOF AND MANIPULATION TCMP
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PROGRAM PROOF AND MANIPULATION (G1.
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PROGRAM PROOF AND MANIPULATION Diff
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PROGRAM PROOF AND MANIPULATION APPE
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PROGRAM PROOF AND MANIPULATION CA 0
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PROGRAM PROOF AND MANIPULATION IIII
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4 Building-in Equational Theories G
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PLOTKIN be carried through using th
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PLOTKIN constants — the numerals
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PLOTKIN We use the equality symbol
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PLOTKIN If /(S)> 0, there is a lite
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PLOTKIN from Var (82) u V and for e
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and terms ti, ui (i=1, n), t, u, v'
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PLOTKIN not checked, one can obtain
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PLOTKIN infinite set of refutations
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5 Theorem Proving in Arithmetic wit
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COOPER Step 3 Simplify each relatio
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COOPER but F(x0) is true, then at l
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COOPER where R1, . R. are arithmeti
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COOPER amples with the same relatio
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COMPUTATIONAL LOGIC the resolvent R
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COMPUTATIONAL LOGIC The value of th
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COMPUTATIONAL LOGIC The value of TE
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COMPUTATIONAL LOGIC (z, 7 . (f x y)
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COMPUTATIONAL LOGIC GETLIT(CL,K). G
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01001 IVNOIIVII1dPIO3 c4 c6 c3 ((+(
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COMPUTATIONAL LOGIC the figure exhi
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COMPUTATIONAL LOGIC structure shari
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COMPUTATIONAL LOGIC A clause is a H
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COMPUTATIONAL LOGIC resolution have
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COMPUTATIONAL LOGIC It should be no
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• COMPUTATIONAL LOGIC Since both
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COMPUTATIONAL LOGIC assumption, m +
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COMPUTATIONAL LOGIC generated by th
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COMPUTATIONAL LOGIC are two (not ne
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COMPUTATIONAL LOGIC Since these two
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COMPUTATIONAL LOGIC Figure 1. Loop
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COMPUTATIONAL LOGIC talking about u
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INFERENTIAL AND HEURISTIC SEARCH
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INFERENTIAL AND HEURISTIC SEARCH de
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INFERENTIAL AND HEURISTIC SEARCH mu
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INFERENTIAL AND HEURISTIC SEARCH by
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INFERENTIAL AND HEURISTIC SEARCH ti
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INFERENTIAL AND HEURISTIC SEARCH co
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INFERENTIAL AND HEURISTIC SEARCH co
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C) o o C) ri\41 141‘ • • —
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INFERENTIAL AND HEURISTIC SEARCH ne
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INFERENTIAL AND HEURISTIC SEARCH Mo
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INFERENTIAL AND HEURISTIC SEARCH Ta
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INFERENTIAL AND HEURISTIC SEARCH Ac
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INFERENTIAL AND HEURISTIC SEARCH Q
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10 And-or Graphs, Theorem-proving G
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KOWALSKI specify a search space of
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KOWALSKI sharing a single copy of a
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KOWALSKI A derivation D is a finite
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KOWALSKI goals to be solved simulta
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KOWALSKI to select and generate can
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KOWALSKI Figure 5. The search space
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KOWALSKI (1) size, the number of se
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KOWALSKI =h. A diagonal search stra
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KOWALSKI Theorem do not specify wha
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KOWALSKI solution path whose additi
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KOWALSKI carry no indication of how
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KOWALSKI within which unit preferen
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KOWALSKI municating special-purpose
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11 An Approach to the Frame Problem
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SAND EWALL principle be re-proved f
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SANDEWALL used in the reasoning pro
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SANDEWALL A Unless B and if we wish
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SANDEWALL during the planning phase
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12 A Heuristic Solution to the Tang
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DEUTSCII AND HAYES puzzles is Read'
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DEUTSCH AND HAYES application of al
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DEUTSCH AND HAYE quite early on tha
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DEUTSCH AND HAYES subpuzzles do not
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DEUTSCII AND IIAYES vertices (or no
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• • DEUTSCH AND HAYES • • .
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DEUTSCH AND HAYES 2 2 2 2 2 • III
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DEUTSCH AND HAYES pieces. Puzzle pi
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DEUTSCH AND HAYES fully described b
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DEUTSCH AND HAYES be fitted into ar
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(2) ciwool] S3s111a.03 1 2110.0102
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DEUTSCH AND HAYES 4.9 Puzzle pieces
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DEUTSCH AND IIAYES r-> direct-match
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DEUTSCII AND HAYES The rules of low
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• .• DEUTSCH AND HAYES It will
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DEUTSCH AND HAYES X .---- x+ I dire
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DEUTSCII AND HAYES •• • •
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13 A Man-Machine Approach for Creat
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KROLAK AND NELSON those who exhibit
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KROLAK AND NELSON normally we expec
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KROLAK AND NELSON tried to capture
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KROLAK AND NELSON V.S.P. 1967) on r
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KROLAK AND NELSON generated overlay
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KROLAK AND NELSON allowed to be zer
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KROLAK AND NELSON Table 1. Loads an
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▪ ts.) Schoo▪ • , •) indica
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Figure 3. Modified initial estimate
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t.) 1 I ■ 1 -- to School 1 --- to
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KROLAK AND NELSON Cost =7301.62 o o
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KROLAK AND NELSON Collins, J.S. (19
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14 Heuristic Theory Formation: Data
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BUCHANAN, FEIGENBAUM AND SRIDHARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDHARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDHARAN
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BUCHANAN, FEIGENBAUM AND SRIDHARAN
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BUCHANAN, FEIGENBAUM AND SRIDIIARAN
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BUCHANAN, FEIGENBAUM AND SRIDHARAN
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PERCEPTUAL AND LINGUISTIC MODELS
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PERCEPTUAL AND LINGUISTIC MODELS An
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PERCEPTUAL AND LINGUISTIC MODELS Re
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PERCEPTUAL AND LINGUISTIC MODELS al
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PERCEPTUAL AND LINGUISTIC MODELS fo
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PERCEPTUAL AND LINGUISTIC MODELS RE
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PERCEPTUAL AND LINGUISTIC MODELS "T
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PERCEPTUAL AND LINGUISTIC MODELS TR
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PERCEPTUAL AND LINGUISTIC MODELS no
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PERCEPTUAL AND LINGUISTIC MODELS Pr
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PERCEPTUAL AND LINGUISTIC MODELS de
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PERCEPTUAL AND LINGUISTIC MODELS di
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PERCEPTUAL AND LINGUISTIC MODELS it
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PERCEPTUAL AND LINGUISTIC MODELS (0
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PERCEPTUAL AND LINGUISTIC MODELS 9a
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PERCEPTUAL AND LINGUISTIC MODELS fr
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PERCEPTUAL AND LINGUISTIC MODELS in
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PERCEPTUAL AND LINGUISTIC MODELS ex
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PERCEPTUAL AND LINGUISTIC MODELS Th
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PERCEPTUAL AND LINGUISTIC MODELS di
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PERCEPTUAL AND LINGUISTIC MODELS My
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PERCEPTUAL AND LINGUISTIC MODELS BR
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PERCEPTUAL AND LINGUISTIC MODELS se
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PERCEPTUAL AND LINGUISTIC MODELS I
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18 The Syntactic Inference Problem
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HERMAN AND WALKER 1971a,b). In this
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HERMAN AND WALKER For any DpxL-sche
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HERMAN AND WALKER symbols from Go o
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HERMAN AND WALKER facts arise which
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HERMAN AND WALKER of strings which
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HERMAN AND WALKER We shall demonstr
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HERMAN AND WALKER unbounded growth
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19 Parallel and Serial Methods of P
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W1LLSHAW AND BUNEMAN Each pattern i
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WILLSHAW AND BUNEMAN this means tha
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WILLSHAW AND BUNEMAN 4. GRAPHICAL R
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WILLSHAW AND BUNEMAN (3) Given a se
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WILLSHAW AND BUNEMAN taking the for
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PERCEPTUAL AND LINGUISTIC MODELS be
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PERCEPTUAL AND LINGUISTIC MODELS (a
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PERCEPTUAL AND LINGUISTIC MODELS R=
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PERCEPTUAL AND LINGUISTIC MODELS Us
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PERCEPTUAL AND LINGUISTIC MODELS co
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PERCEPTUAL AND LINGUISTIC MODELS on
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PERCEPTUAL AND LINGUISTIC MODELS wi
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PERCEPTUAL AND LINGUISTIC MODELS Fi
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PROBLEM-SOLVING AUTOMATA
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PROBLEM-SOLVING AUTOMATA drawn from
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PROBLEM-SOLVING AUTOMATA vi(fZ, .,
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PROBLEM-SOLVING AUTOMATA with the d
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PROBLEM-SOLVING AUTOMATA Consider t
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PROBLEM-SOLVING AUTOMATA of the equ
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PROBLEM-SOLVING AUTOMATA estimate t
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PROBLEM-SOLVING AUTOMATA automata--
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PROBLEM-SOLVING AUTOMATA automata -
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23 Some New Directions in Robot Pro
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FIKES, HART AND NILSSON to be achie
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FIKES, HART AND NILSSON There are s
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FIKES, HART AND NILSSON (2) The rea
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FIKES, HART AND NILSSON To progress
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FIKES, HART AND NILSSON actions of
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FIKES, HART AND NILSSON posed by a
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FIKES, HART AND NILSSON indicates t
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FIKES, HART AND NILSSON CHECKDOOR (
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FIKES, HART AND NILSSON To make the
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FIKES, HART AND NILSSON team robot
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FIKES, HART AND NILSSON posed in pr
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FIKES, HART AND NILSSON definer is
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24 The MIT Robot P. H. Winston Arti
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WINsTON ARROW FORK PSI Figure 2. Th
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WINSTON Figure 6. The fork vertex c
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WINSTON time. The issue was avoided
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WINSTON Given this fact a simple se
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WINSTON less ambiguity than does a
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WINSTON Each action module is charg
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WINSTON mysterious at first. But, l
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WINSTON alternative of using clues
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WINSTON generates in the machine an
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WINSTON objects and the pointers re
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WINSTON current implementation, dif
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WINSTON better by devising a little
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WINSTON 2. A large network of progr
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WINSTON FIND-ALTITUDE. This determi
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FIND-DIMENSIONS WINSTON MANIPULATE
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WINSTON Hewitt, C. (1972) Descripti
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PROBLEM-SOLVING AUTOMATA the system
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PROBLEM-SOLVING AUTOMATA Figure I I
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PROBLEM-SOLVING AUTOMATA resolution
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PROBLEM-SOLVING AUTOMATA Unit A pla
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PROBLEM-SOLVING AUTOMATA rotate abo
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PROBLEM-SOLVING AUTOMATA HREG(N); R
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PROBLEM-SOLVING AUTOMATA • DISPLA
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PROBLEM-SOLVING AUTOMATA the ii316
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INDEX Deutsch 205-40 grammar 293-5,
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• INDEX Pavlidis 307, 323 Paz 352