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WILL AN EXPERT SYSTEM WORK FOR MY PROBLEM? 309 Figure 9.2 Troubleshooting the system start-up for Macintosh computers the cause of the system’s malfunction. This is essentially data-driven reasoning, which can be best realised with the forward-chaining inference technique. The expert system should first ask the user to select a particular task, and once the task is selected, the system should direct troubleshooting by asking the user for additional information until the fault is found. Let us develop a general rule structure. In each rule, we need to include a clause that identifies the current task. Since our prototype is limited to the
310 KNOWLEDGE ENGINEERING AND DATA MINING Mac system start-up, the first clause of all rules will identify this task. For example, Rule: 1 if task is ‘system start-up’ then ask problem Rule: 2 if task is ‘system start-up’ and problem is ‘system does not start’ then ask ‘test power cords’ Rule: 3 if task is ‘system start-up’ and problem is ‘system does not start’ and ‘test power cords’ is ok then ask ‘test Powerstrip’ All the other rules will follow this structure. A set of rules to direct troubleshooting when the Mac system does not start (in Leonardo code) is shown in Figure 9.3. Now we are ready to build a prototype, or in other words to implement the initial set of rules using an expert system development tool. How do we choose an expert system development tool? In general, we should match the features of the problem with the capabilities of the tool. These tools range from high-level programming languages such as LISP, PROLOG, OPS, C and Java, to expert system shells. High-level programming languages offer a greater flexibility and can enable us to meet any project requirements, but they do require high-level programming skills. On the other hand, shells, although they do not have the flexibility of programming languages, provide us with the built-in inference engine, explanation facilities and the user interface. We do not need any programming skills to use a shell – we just enter rules in English in the shell’s knowledge base. This makes shells particularly useful for rapid prototyping. So how do we choose a shell? The Appendix provides some details of a few commercial expert systems shells currently available on the market. This can help you to choose an appropriate tool; however the internet is rapidly becoming the most valuable source of information. Many vendors have Web sites, and you can even try and evaluate their products over the Web. In general, when selecting an expert system shell, you should consider how the shell represents knowledge (rules or frames), what inference mechanism it uses (forward or backward chaining), whether the shell supports inexact reasoning and if so what technique it uses (Bayesian reasoning, certainty factors or fuzzy logic), whether the shell has an ‘open’ architecture allowing access to
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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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Page 317 and 318: 298 HYBRID INTELLIGENT SYSTEMS 4 De
Page 320 and 321: Knowledge engineering and data mini
Page 322 and 323: INTRODUCTION, OR WHAT IS KNOWLEDGE
Page 330 and 331: WILL AN EXPERT SYSTEM WORK FOR MY P
Page 336 and 337: WILL A FUZZY EXPERT SYSTEM WORK FOR
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Page 356 and 357: WILL GENETIC ALGORITHMS WORK FOR MY
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Page 368 and 369: DATA MINING AND KNOWLEDGE DISCOVERY
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DATA MINING AND KNOWLEDGE DISCOVERY
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SUMMARY 361 9.8 Summary In this cha
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REFERENCES 363 7 Why are fuzzy syst
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Glossary The glossary entries are c
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GLOSSARY 367 Back-propagation see B
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GLOSSARY 369 Clipping A common meth
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GLOSSARY 371 amounts of data in ord
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GLOSSARY 373 Evolution strategy A n
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GLOSSARY 375 Fuzzy rule A condition
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GLOSSARY 377 Heuristic search A sea
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GLOSSARY 379 Knowledge base A basic
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GLOSSARY 381 Method A procedure ass
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GLOSSARY 383 Output layer The last
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GLOSSARY 385 Roulette wheel selecti
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GLOSSARY 387 the network differs fr
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GLOSSARY 389 determines the strengt
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392 AI TOOLS AND VENDORS EXSYS, Inc
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394 AI TOOLS AND VENDORS M.4 A powe
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396 AI TOOLS AND VENDORS CynapSys,
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398 AI TOOLS AND VENDORS 215 Parkwa
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400 AI TOOLS AND VENDORS text files
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402 AI TOOLS AND VENDORS TransferTe
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404 AI TOOLS AND VENDORS La Jolla,
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406 AI TOOLS AND VENDORS Fax: +1 (3
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408 INDEX belongs-to 137-8 Berkeley
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410 INDEX fuzzification 107 fuzzifi
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412 INDEX Mamdani, E. 20, 106 Mamda
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INDEX 415 unsupervised learning 200
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