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MICHAEL NEGNEVITSKY Artificial Inte
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We work with leading authors to dev
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Pearson Education Limited Edinburgh
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Contents Preface Preface to the sec
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CONTENTS ix 7 Evolutionary computat
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Preface ‘The only way not to succ
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PREFACE xiii In Chapter 3, we prese
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Prefacetothesecondedition The main
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Introduction to knowledgebased inte
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INTELLIGENT MACHINES 3 Figure 1.1 T
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THE HISTORY OF ARTIFICIAL INTELLIGE
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THE HISTORY OF ARTIFICIAL INTELLIGE
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THE HISTORY OF ARTIFICIAL INTELLIGE
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THE HISTORY OF ARTIFICIAL INTELLIGE
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THE HISTORY OF ARTIFICIAL INTELLIGE
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THE HISTORY OF ARTIFICIAL INTELLIGE
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SUMMARY 17 Although fuzzy systems a
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SUMMARY 19 Table 1.1 Period A summa
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QUESTIONS FOR REVIEW 21 or fuzzy se
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REFERENCES 23 Kosko, B. (1997). Fuz
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Rule-based expert systems 2 In whic
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RULES AS A KNOWLEDGE REPRESENTATION
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THE MAIN PLAYERS IN THE EXPERT SYST
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STRUCTURE OF A RULE-BASED EXPERT SY
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FUNDAMENTAL CHARACTERISTICS OF AN E
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FORWARD AND BACKWARD CHAINING INFER
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FORWARD AND BACKWARD CHAINING INFER
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FORWARD AND BACKWARD CHAINING INFER
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA: A DEMONSTRATION RULE-BASED E
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CONFLICT RESOLUTION 47 Does MEDIA A
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CONFLICT RESOLUTION 49 . Fire the m
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SUMMARY 51 . Dealing with incomplet
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QUESTIONS FOR REVIEW 53 . Expert sy
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Uncertainty management in rule-base
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BASIC PROBABILITY THEORY 57 Table 3
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BASIC PROBABILITY THEORY 59 single
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BAYESIAN REASONING 61 Figure 3.1 Th
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BAYESIAN REASONING 63 places an eno
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FORECAST: BAYESIAN ACCUMULATION OF
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FORECAST: BAYESIAN ACCUMULATION OF
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FORECAST: BAYESIAN ACCUMULATION OF
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FORECAST: BAYESIAN ACCUMULATION OF
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BIAS OF THE BAYESIAN METHOD 73 Doma
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CERTAINTY FACTORS THEORY AND EVIDEN
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CERTAINTY FACTORS THEORY AND EVIDEN
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- Page 102 and 103: SUMMARY 83 team also could assume t
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- Page 108 and 109: FUZZY SETS 89 Range of logical valu
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- Page 112 and 113: FUZZY SETS 93 Figure 4.3 Crisp (a)
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- Page 116 and 117: OPERATIONS OF FUZZY SETS 97 Table 4
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- Page 126 and 127: FUZZY INFERENCE 107 determined by f
- Page 128 and 129: FUZZY INFERENCE 109 However, the OR
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- Page 132 and 133: FUZZY INFERENCE 113 Figure 4.15 The
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- Page 144 and 145: SUMMARY 125 4.8 Summary In this cha
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- Page 150 and 151: Frame-based expert systems 5 In whi
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- Page 162 and 163: METHODS AND DEMONS 143 Figure 5.7 I
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- Page 180 and 181: SUMMARY 161 Figure 5.19 The WHEN CH
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- Page 186 and 187: INTRODUCTION, OR HOW THE BRAIN WORK
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- Page 190 and 191: THE PERCEPTRON 171 Figure 6.6 Linea
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MULTILAYER NEURAL NETWORKS 179 k
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MULTILAYER NEURAL NETWORKS 181 Figu
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MULTILAYER NEURAL NETWORKS 183 Figu
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ACCELERATED LEARNING IN MULTILAYER
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ACCELERATED LEARNING IN MULTILAYER
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THE HOPFIELD NETWORK 189 Figure 6.1
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THE HOPFIELD NETWORK 191 What deter
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THE HOPFIELD NETWORK 193 fundamenta
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THE HOPFIELD NETWORK 195 The Hopfie
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BIDIRECTIONAL ASSOCIATIVE MEMORY 19
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BIDIRECTIONAL ASSOCIATIVE MEMORY 19
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SELF-ORGANISING NEURAL NETWORKS 201
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SELF-ORGANISING NEURAL NETWORKS 203
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SELF-ORGANISING NEURAL NETWORKS 205
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SELF-ORGANISING NEURAL NETWORKS 207
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SELF-ORGANISING NEURAL NETWORKS 209
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SELF-ORGANISING NEURAL NETWORKS 211
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SUMMARY 213 . In the 1940s, Warren
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QUESTIONS FOR REVIEW 215 neuron. Al
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REFERENCES 217 Kosko, B. (1992). Ne
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220 EVOLUTIONARY COMPUTATION biolog
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222 EVOLUTIONARY COMPUTATION 7.3 Ge
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224 EVOLUTIONARY COMPUTATION and th
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226 EVOLUTIONARY COMPUTATION when t
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228 EVOLUTIONARY COMPUTATION The fi
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230 EVOLUTIONARY COMPUTATION being
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232 EVOLUTIONARY COMPUTATION 7.4 Wh
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234 EVOLUTIONARY COMPUTATION Thus,
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236 EVOLUTIONARY COMPUTATION Table
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238 EVOLUTIONARY COMPUTATION The ev
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240 EVOLUTIONARY COMPUTATION Figure
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242 EVOLUTIONARY COMPUTATION Figure
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244 EVOLUTIONARY COMPUTATION Figure
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246 EVOLUTIONARY COMPUTATION writte
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248 EVOLUTIONARY COMPUTATION Step 4
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250 EVOLUTIONARY COMPUTATION the se
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252 EVOLUTIONARY COMPUTATION Figure
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254 EVOLUTIONARY COMPUTATION 7.8 Su
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256 EVOLUTIONARY COMPUTATION 9 Draw
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Hybrid intelligent systems 8 In whi
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NEURAL EXPERT SYSTEMS 261 Table 8.1
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NEURAL EXPERT SYSTEMS 263 In rule-b
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NEURAL EXPERT SYSTEMS 265 Now by at
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NEURAL EXPERT SYSTEMS 267 The rule
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NEURO-FUZZY SYSTEMS 269 structures
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NEURO-FUZZY SYSTEMS 271 Figure 8.5
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NEURO-FUZZY SYSTEMS 273 by using th
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NEURO-FUZZY SYSTEMS 275 Figure 8.8
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ANFIS: ADAPTIVE NEURO-FUZZY INFEREN
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ANFIS: ADAPTIVE NEURO-FUZZY INFEREN
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ANFIS: ADAPTIVE NEURO-FUZZY INFEREN
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ANFIS: ADAPTIVE NEURO-FUZZY INFEREN
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EVOLUTIONARY NEURAL NETWORKS 285 Fi
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EVOLUTIONARY NEURAL NETWORKS 287 Fi
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EVOLUTIONARY NEURAL NETWORKS 289 ba
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FUZZY EVOLUTIONARY SYSTEMS 291 Figu
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FUZZY EVOLUTIONARY SYSTEMS 293 Figu
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FUZZY EVOLUTIONARY SYSTEMS 295 How
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QUESTIONS FOR REVIEW 297 can deal w
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REFERENCES 299 Jang, J.-S.R. (1993)
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302 KNOWLEDGE ENGINEERING AND DATA
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304 KNOWLEDGE ENGINEERING AND DATA
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306 KNOWLEDGE ENGINEERING AND DATA
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308 KNOWLEDGE ENGINEERING AND DATA
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310 KNOWLEDGE ENGINEERING AND DATA
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312 KNOWLEDGE ENGINEERING AND DATA
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314 KNOWLEDGE ENGINEERING AND DATA
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316 KNOWLEDGE ENGINEERING AND DATA
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318 KNOWLEDGE ENGINEERING AND DATA
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320 KNOWLEDGE ENGINEERING AND DATA
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322 KNOWLEDGE ENGINEERING AND DATA
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324 KNOWLEDGE ENGINEERING AND DATA
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326 KNOWLEDGE ENGINEERING AND DATA
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328 KNOWLEDGE ENGINEERING AND DATA
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330 KNOWLEDGE ENGINEERING AND DATA
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332 KNOWLEDGE ENGINEERING AND DATA
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334 KNOWLEDGE ENGINEERING AND DATA
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336 KNOWLEDGE ENGINEERING AND DATA
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338 KNOWLEDGE ENGINEERING AND DATA
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340 KNOWLEDGE ENGINEERING AND DATA
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342 KNOWLEDGE ENGINEERING AND DATA
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344 KNOWLEDGE ENGINEERING AND DATA
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346 KNOWLEDGE ENGINEERING AND DATA
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348 KNOWLEDGE ENGINEERING AND DATA
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350 KNOWLEDGE ENGINEERING AND DATA
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352 KNOWLEDGE ENGINEERING AND DATA
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354 KNOWLEDGE ENGINEERING AND DATA
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356 KNOWLEDGE ENGINEERING AND DATA
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358 KNOWLEDGE ENGINEERING AND DATA
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360 KNOWLEDGE ENGINEERING AND DATA
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362 KNOWLEDGE ENGINEERING AND DATA
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364 KNOWLEDGE ENGINEERING AND DATA
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366 GLOSSARY a-part-of An arc (also
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368 GLOSSARY Byte A set of eight bi
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370 GLOSSARY Connection A link from
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372 GLOSSARY marked a major ‘para
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374 GLOSSARY cells.) A tax form, fo
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376 GLOSSARY Genetic operator An op
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378 GLOSSARY Initialisation The fir
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380 GLOSSARY Linear activation func
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382 GLOSSARY Also, a basic processi
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384 GLOSSARY Premise see Antecedent
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386 GLOSSARY Schema A bit string of
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388 GLOSSARY Training set A data se
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Appendix: AI tools and vendors Expe
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AI TOOLS AND VENDORS 393 Phone: +1
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AI TOOLS AND VENDORS 395 generates
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AI TOOLS AND VENDORS 397 TransferTe
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AI TOOLS AND VENDORS 399 rFLASH rFL
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AI TOOLS AND VENDORS 401 The MathWo
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AI TOOLS AND VENDORS 403 Phone: +1
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AI TOOLS AND VENDORS 405 GEATbx The
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Index A accelerated learning 185-8
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INDEX 409 Davis, L. 337 Davis’s l
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INDEX 411 intelligent behaviour tes
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INDEX 413 odds 67-8 posterior 68 pr