AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

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AI TOOLS AND VENDORS 395 generates a decision tree. XpertRule also uses fuzzy reasoning, which can be integrated with crisp reasoning and with GA optimisation. Attar Software UK Intellicrafters (Attar Software USA) Newlands Road Renaissance International Corporation Leigh WN7 4HN, England Newburyport, MA 01950, USA Phone: +44 (0) 870-60-60-870 Phone: +1 (978) 465-5111 Fax: +44 (0) 870-60-40-156 Fax: +1 (978) 465-0666 e-mail: info@attar.co.uk e-mail: info@IntelliCrafters.com http://www.attar.com/ Fuzzy logic tools CubiCalc A software tool for creating and using fuzzy rules. With CubiCalc, the user can write English-like IF-THEN rules and use a graphical editor to define fuzzy sets. The user can then apply the rules to data or use them in a simulated dynamic scenario. CubiCalc is particularly useful for rapid prototyping. No programming is needed to set up plots, numeric displays, input and output data files, and interactive data-entry windows. HyperLogic Corporation PO Box 300010 Escondido, CA 92030-0010, USA Phone: +1 (760) 746-2765 Fax: +1 (760) 746-4089 http://www.hyperlogic.com/cbc.html FIDE The Fuzzy Inference Development Environment (FIDE) is a complete environment for developing a fuzzy system. It supports all phases of the development process, from the concept to the implementation. FIDE serves as the developer’s guide in creating a fuzzy controller, including its implementation as a software or hardware solution. Hardware solutions are realised in the Motorola microcontroller units; the code is generated automatically. FIDE also supports C code by creating ANSI C code for a fuzzy inference unit. Aptronix, Inc. PO Box 70188 Sunnyvale, CA 94086-0188, USA Phone: +1 (408) 261-1898 Fax: +1 (408) 490-2729 e-mail: support@aptronix.com http://www.aptronix.com/fide/ FlexTool FlexTool offers the Genetic Algorithm, Neural Network and Fuzzy System MATLAB Toolbox for building intelligent systems. The readable full-source code is included with the toolbox; it can be easily customised as well as tailored to the user’s needs. FlexTool (Fuzzy System) facilitates the development of fuzzy expert systems, fuzzy predictors and fuzzy controllers. It provides a graphical user interface for tuning membership functions.
396 AI TOOLS AND VENDORS CynapSys, LLC 160 Paradise Lake Road Birmingham, AL 35244, USA Phone/Fax: +1 (530) 325-9930 e-mail: info@cynapsys.com http://www.flextool.com/ FLINT The Fuzzy Logic INferencing Toolkit (FLINT) is a versatile fuzzy logic inference system that makes fuzzy rules available within a sophisticated programming environment. FLINT supports the concepts of fuzzy variables, fuzzy qualifiers and fuzzy modifiers (linguistic hedges). Fuzzy rules are expressed in a simple, uncluttered syntax. Furthermore, they can be grouped into matrices, commonly known as fuzzy associative memory (FAM). FLINT provides a comprehensive set of facilities for programmers to construct fuzzy expert systems and decision-support applications on all LPA-supported hardware and software platforms. Logic Programming Associates Ltd Studio 4, RVPB, Trinity Road London SW18 3SX, England Phone: +44 (0) 208-871-2016 Fax: +44 (0) 208-874-0449 e-mail: support@lpa.co.uk http://www.lpa.co.uk/ FuzzyCLIPS FuzzyCLIPS is an extension of the CLIPS (C Language Integrated Production System) from NASA, which has been widely distributed for a number of years. It enhances CLIPS by providing a fuzzy reasoning capability such that the user can represent and manipulate fuzzy facts and rules. FuzzyCLIPS can deal with exact, fuzzy and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. The system uses two basic inexact concepts: fuzziness and uncertainty. FuzzyCLIPS is available as a free download. Integrated Reasoning Group NRC Institute for Information Technology 1200 Montreal Road, Building M-50 Ottawa, ON Canada, K1A 0R6 Phone: +1 (613) 993-8557 Fax: +1 (613) 952-0215 e-mail: Bob.Orchard@nrc-cnrc.gc.ca http://ai.iit.nrc.ca/IR_public/fuzzy/fuzzyClips/fuzzyCLIPSIndex.html Fuzzy Control Manager The Fuzzy Control Manager (FCM) provides a graphical user interface (GUI) that allows the user to display any relevant data while developing, debugging and optimising a fuzzy system. Offers the point-and-click rule editor and graphical editor of membership functions. The FCM enables the user to generate a source code in C assembler or binary codes.
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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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Page 368 and 369: DATA MINING AND KNOWLEDGE DISCOVERY
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 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: 394 AI TOOLS AND VENDORS M.4 A powe
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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