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GLOSSARY 381 Method A procedure associated with an attribute of a frame. A method can determine the attribute’s value or execute a series of actions when the attribute’s value changes. Most frame-based expert systems use two types of methods: WHEN CHANGED and WHEN NEEDED. Method and demon are often used as synonyms. [es] Momentum constant A positive constant less than unity included in the delta rule. The use of momentum accelerates learning in a multilayer perceptron and helps to prevent it from getting caught in a local minimum. [nn] Multilayer perceptron The most common topology of an ANN in which perceptrons are connected together to form layers. A multilayer perceptron has the input layer, at least one hidden layer and the output layer. The most popular method of training a multilayer perceptron is backpropagation. [nn] Multiple inheritance The ability of an object or a frame to inherit information from multiple superclasses. [es] Mutation A genetic operator that randomly changes the gene value in a chromosome. [ec] Mutation probability A number between zero and one that indicates the probability of mutation occurring in a single gene. [ec] MYCIN A classic rule-based expert system developed in the 1970s for the diagnosis of infectious blood diseases. The system used certainty factors for managing uncertainties associated with knowledge in medical diagnosis. [es] Natural selection The process by which the most fit individuals have a better chance to mate and reproduce, and thereby to pass their genetic material on to the next generation. [ec] Neural computing A computational approach to modelling the human brain that relies on connecting a large number of simple processors to produce complex behaviour. Neural computing can be implemented on specialised hardware or with software, called artificial neural networks, that simulates the structure and functions of the human brain on a conventional computer. [nn] Neural network A system of processing elements, called neurons, connected together to form a network. The fundamental and essential characteristic of a biological neural network is the ability to learn. Artificial neural networks also have this ability; they are not programmed, but learn from examples through repeated adjustments of their weights. [nn] Neuron A cell that is capable of processing information. A typical neuron has many inputs (dendrites) and one output (axon). The human brain contains roughly 10 12 neurons. dm = data mining ke = knowledge engineering
382 GLOSSARY Also, a basic processing element of an ANN that computes the weighted sum of the input signals and passes the result through its activation function to generate an output. [nn] Node A decision point of a decision tree. [dm] Noise A random external disturbance that affects a transmitted signal. Noisy data contain errors associated with the way the data was collected, measured and interpreted. [dm] NOT A logical operator used for representing the negation of a statement. [es] Object A concept, abstraction or thing that can be individually selected and manipulated, and that has some meaning for the problem at hand. All objects have identity and are clearly distinguishable. Michael Black, Audi 5000 Turbo, IBM Aptiva S35 are examples of objects. In object-oriented programming, an object is a self-contained entity that consists of both data and procedures to manipulate the data. [es] Object-oriented programming A programming method that uses objects as a basis for analysis, design and implementation. [es] Offspring An individual that was produced through reproduction. Also referred to as a child. [ec] Operational database A database used for the daily operation of an organisation. Data in operational databases is regularly updated. [dm] OPS A high-level programming language derived from LISP for developing rule-based expert systems. [es] Optimisation An iterative process of improving the solution to a problem with respect to a specified objective function. [ec] OR A logical operator; when used in a production rule, it implies that if any of the antecedents joined with OR is true, then the rule consequent must also be true. [es] Overfitting A state in which an ANN has memorised all the training examples, but cannot generalise. Overfitting may occur if the number of hidden neurons is too big. The practical approach to preventing overfitting is to choose the smallest number of hidden neurons that yields good generalisation. Also referred to as Over-training. [nn] Over-training see Overfitting. [nn] es = expert systems fl = fuzzy logic nn = neural networks ec = evolutionary computation
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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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Page 380 and 381: SUMMARY 361 9.8 Summary In this cha
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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 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 and 428: 408 INDEX belongs-to 137-8 Berkeley
Page 429 and 430: 410 INDEX fuzzification 107 fuzzifi
Page 431 and 432: 412 INDEX Mamdani, E. 20, 106 Mamda
Page 434: INDEX 415 unsupervised learning 200
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