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INDEX 409 Davis, L. 337 Davis’s law 301 dBASE III 150, 152 De Morgan’s Laws 102–3 debugging aids 33 decision support system 318–23, 340–5 decision tree 352–3 branch 352 child node 352 dependent variable 352–3 leaf 352 parent node 352 root node 352 split 353 Deep Blue 165 defuzzification 111–12, 273 centroid technique 111 sum-product composition 273 defuzzification layer 273, 279 degree of confidence, see certainty factor degree of membership 92 delta rule 172 generalised 185 demon 133, 142 DENDRAL 9–10, 12, 19, 40, 307 dendrite 166 dependent variable 352–3 developer interface 32–3 debugging aids 33 input/output facilities 33 knowledge base editor 32–3 diagnosis 303, 308–12, 340–5 dilation 95–6 discrete data 330 disjunction 26, 107 distributivity 101 domain expert, see expert dummy rule 294 E EBCDIC code 304 Edmonds, D. 5 Electronic Numerical Integrator and Calculator 5, 19 EMYCIN 10, 19 end-user, see user Enigma 2 epoch 172, 183 error gradient 178 essential property 140 Euclidean distance 207–8, 334 evidential reasoning 74–80, 315–17 evolution 219–21 evolution strategy 14, 242–4 (1+1)-evolution strategy 242–4 evolutionary computation 14, 219 evolutionary fitness 220 evolutionary neural network 285–7 exclusive-OR 172, 180–1, 184–5, 275–6 exhaustive search 51 expert 25, 29, 56, 67, 73, 87 expert system 8–12, 28, 33–5 frame-based 149–61 fuzzy 114–24, 317–22 neural 262–8 rule-based 30–3, 41–7, 50–1, 308–17 expert system shell 28, 310–12 explanation facilities 32, 263 how 32 why 32 external interface 32 F facet 133 inference facet 134 prompt facet 134 search order facet 149 value facet 133 fact 31 FAM, see fuzzy associative memory feedback neural network 188–9 feedforward neural network 175 Feigenbaum E. 9, 10, 19 Feller, W. 57 Fine, T. 57 firing a rule 31, 36, 146 first-order Sugeno fuzzy model 277 fit-vector 94 fitness 221 fitness function 222, 237–8, 338–9 Fogel, D. 20 forgetting factor 201–2 FORTRAN 6, 30, 32, 245, 253 forward chaining 37–8, 309 frame 131–3 class 134–5 instance 134–5 frame-based expert system 149–61 fully connected neural network 176 fundamental memory 192–3
410 INDEX fuzzification 107 fuzzification layer 269, 278 fuzzy associative memory 118 fuzzy evolutionary system 290–6 fuzzy expert system 114–24, 317–22 fuzzy grid 290–1 fuzzy inference 106–14 Mamdani 106–12, 114 Sugeno 112–14 Fuzzy Knowledge Builder 121 fuzzy logic 15–17, 87–9 fuzzy reasoning 104–5 fuzzy rule 15, 103–6, 112 fuzzy rule layer 271–2, 278–9 fuzzy rule table 119, 291 multiple 293 fuzzy set 90–4 clipping 110 scaling 110 fuzzy set theory, see fuzzy logic fuzzy singleton 112 fuzzy thinking 87–8 fuzzy variable 94–5 G gain chart 354, 356 gene 221, 237 General Problem Solver 6, 19 generalisation 137, 325, 329 generalised activity product rule 202 generalised delta rule 185 generation 222 genetic algorithm 14, 222–3, 295–6, 336–9 convergence 229 performance 229–31, 338–9 genetic operator 221, 226, 337–8 cloning 226 crossover 221, 226, 248–9, 337–8 mutation 221, 226, 250–1, 338 genetic programming 14, 245–53 genetics 220 Gini, C. 354 Gini coefficient 354 global optimum 229–30 goal 38, 146 goal-driven reasoning, see backward chaining Goldberg, D. 337 GPS, see General Problem Solver Gray coding 229 grid-type fuzzy partition 290–1 Grossberg, S. 13 H Hakel, M. 56 hard limiter 169 Haykin, S. 19 Hebb, D. 200, 214 Hebb’s Law 200–1 Hebbian learning 202–3 hedge 95–7 a little 97 extremely 96, 97 indeed 96, 97 more or less 96, 97 slightly 97 somewhat 97 very 96, 97 very very 96, 97 heuristic 28, 33 hidden layer 175–6 Ho, Y.-C. 13 Holland, J. 14, 20, 221, 232, 254 Hopfield, J. 13, 19, 189, 213 Hopfield network 189–93 architecture 189 convergence 195 fundamental memory 192–3 storage capacity 195–6 training algorithm 193–4 Human Genome Project 350 human expert, see expert hybrid intelligent system 259–60, 340–9 hyperbolic tangent 185 I idempotency 101–2 identity 102 IF-THEN rule, see production rule inference chain 36–7 inference engine 31, 146–7, 262 inheritance 134, 138–41 multiple 140–1 one-parent 138–40 input layer 175, 269, 278 input/output facilities 33 instance-frame 134–5 intelligence 1–2, 219
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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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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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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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BIAS OF THE BAYESIAN METHOD 73 Doma
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CERTAINTY FACTORS THEORY AND EVIDEN
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FORECAST: AN APPLICATION OF CERTAIN
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SUMMARY 83 team also could assume t
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REFERENCES 85 . Both Bayesian reaso
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Fuzzy expert systems 4 In which we
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FUZZY SETS 89 Range of logical valu
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FUZZY SETS 91 Figure 4.2 Crisp (a)
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FUZZY SETS 93 Figure 4.3 Crisp (a)
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LINGUISTIC VARIABLES AND HEDGES 95
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OPERATIONS OF FUZZY SETS 97 Table 4
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OPERATIONS OF FUZZY SETS 99 Similar
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OPERATIONS OF FUZZY SETS 101 Figure
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FUZZY RULES 103 Example: NOT (tall
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FUZZY RULES 105 Figure 4.9 Monotoni
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FUZZY INFERENCE 107 determined by f
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FUZZY INFERENCE 109 However, the OR
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FUZZY INFERENCE 111 Step 4: Defuzzi
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FUZZY INFERENCE 113 Figure 4.15 The
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BUILDING A FUZZY EXPERT SYSTEM 115
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SUMMARY 125 4.8 Summary In this cha
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BIBLIOGRAPHY 127 9 What is clipping
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BIBLIOGRAPHY 129 Zadeh, L.A. (1975)
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132 FRAME-BASED EXPERT SYSTEMS Figu
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134 FRAME-BASED EXPERT SYSTEMS of a
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136 FRAME-BASED EXPERT SYSTEMS - -
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140 FRAME-BASED EXPERT SYSTEMS and
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142 FRAME-BASED EXPERT SYSTEMS such
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146 FRAME-BASED EXPERT SYSTEMS valu
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150 FRAME-BASED EXPERT SYSTEMS In a
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156 FRAME-BASED EXPERT SYSTEMS illu
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158 FRAME-BASED EXPERT SYSTEMS Area
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160 FRAME-BASED EXPERT SYSTEMS ) On
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162 FRAME-BASED EXPERT SYSTEMS The
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164 FRAME-BASED EXPERT SYSTEMS Bibl
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166 ARTIFICIAL NEURAL NETWORKS What
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168 ARTIFICIAL NEURAL NETWORKS Tabl
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170 ARTIFICIAL NEURAL NETWORKS Figu
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172 ARTIFICIAL NEURAL NETWORKS Step
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174 ARTIFICIAL NEURAL NETWORKS In F
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176 ARTIFICIAL NEURAL NETWORKS Why
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178 ARTIFICIAL NEURAL NETWORKS To p
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180 ARTIFICIAL NEURAL NETWORKS (b)
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182 ARTIFICIAL NEURAL NETWORKS Then
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184 ARTIFICIAL NEURAL NETWORKS The
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192 ARTIFICIAL NEURAL NETWORKS In o
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194 ARTIFICIAL NEURAL NETWORKS wher
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198 ARTIFICIAL NEURAL NETWORKS In t
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200 ARTIFICIAL NEURAL NETWORKS Ther
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204 ARTIFICIAL NEURAL NETWORKS Here
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206 ARTIFICIAL NEURAL NETWORKS The
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214 ARTIFICIAL NEURAL NETWORKS phys
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216 ARTIFICIAL NEURAL NETWORKS 10 D
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Evolutionary computation 7 In which
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SIMULATION OF NATURAL EVOLUTION 221
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GENETIC ALGORITHMS 223 Figure 7.2 A
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GENETIC ALGORITHMS 225 Figure 7.3 T
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GENETIC ALGORITHMS 227 Figure 7.5 T
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GENETIC ALGORITHMS 229 When necessa
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GENETIC ALGORITHMS 231 A surface of
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WHY GENETIC ALGORITHMS WORK 233 m x
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MAINTENANCE SCHEDULING WITH GENETIC
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EVOLUTION STRATEGIES 243 Define a s
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GENETIC PROGRAMMING 245 Which metho
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GENETIC PROGRAMMING 247 Table 7.3 T
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GENETIC PROGRAMMING 249 Figure 7.15
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QUESTIONS FOR REVIEW 255 . Evolutio
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BIBLIOGRAPHY 257 Whitley, L.D. (199
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260 HYBRID INTELLIGENT SYSTEMS repr
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262 HYBRID INTELLIGENT SYSTEMS enti
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264 HYBRID INTELLIGENT SYSTEMS Figu
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266 HYBRID INTELLIGENT SYSTEMS Now
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278 HYBRID INTELLIGENT SYSTEMS sets
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280 HYBRID INTELLIGENT SYSTEMS How
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282 HYBRID INTELLIGENT SYSTEMS In t
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292 HYBRID INTELLIGENT SYSTEMS word
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294 HYBRID INTELLIGENT SYSTEMS Step
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296 HYBRID INTELLIGENT SYSTEMS Step
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298 HYBRID INTELLIGENT SYSTEMS 4 De
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Knowledge engineering and data mini
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INTRODUCTION, OR WHAT IS KNOWLEDGE
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WILL AN EXPERT SYSTEM WORK FOR MY P
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WILL A FUZZY EXPERT SYSTEM WORK FOR
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WILL A NEURAL NETWORK WORK FOR MY P
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WILL GENETIC ALGORITHMS WORK FOR MY
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WILL A HYBRID INTELLIGENT SYSTEM WO
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DATA MINING AND KNOWLEDGE DISCOVERY
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Page 378 and 379: DATA MINING AND KNOWLEDGE DISCOVERY
Page 380 and 381: SUMMARY 361 9.8 Summary In this cha
Page 382 and 383: REFERENCES 363 7 Why are fuzzy syst
Page 384 and 385: Glossary The glossary entries are c
Page 386 and 387: GLOSSARY 367 Back-propagation see B
Page 388 and 389: GLOSSARY 369 Clipping A common meth
Page 390 and 391: GLOSSARY 371 amounts of data in ord
Page 392 and 393: GLOSSARY 373 Evolution strategy A n
Page 394 and 395: GLOSSARY 375 Fuzzy rule A condition
Page 396 and 397: GLOSSARY 377 Heuristic search A sea
Page 398 and 399: GLOSSARY 379 Knowledge base A basic
Page 400 and 401: GLOSSARY 381 Method A procedure ass
Page 402 and 403: GLOSSARY 383 Output layer The last
Page 404 and 405: GLOSSARY 385 Roulette wheel selecti
Page 406 and 407: GLOSSARY 387 the network differs fr
Page 408: GLOSSARY 389 determines the strengt
Page 411 and 412: 392 AI TOOLS AND VENDORS EXSYS, Inc
Page 413 and 414: 394 AI TOOLS AND VENDORS M.4 A powe
Page 415 and 416: 396 AI TOOLS AND VENDORS CynapSys,
Page 417 and 418: 398 AI TOOLS AND VENDORS 215 Parkwa
Page 419 and 420: 400 AI TOOLS AND VENDORS text files
Page 421 and 422: 402 AI TOOLS AND VENDORS TransferTe
Page 423 and 424: 404 AI TOOLS AND VENDORS La Jolla,
Page 425 and 426: 406 AI TOOLS AND VENDORS Fax: +1 (3
Page 427: 408 INDEX belongs-to 137-8 Berkeley
Page 431 and 432: 412 INDEX Mamdani, E. 20, 106 Mamda
Page 434: INDEX 415 unsupervised learning 200
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