John Fulcher and Lakhmi C. Jain (Eds.)Computational Intelligence: A CompendiumStudies in Computational Intelligence, Volume 115Editor-in-chiefProf. Janusz KacprzykSystems Research InstitutePolish Academy of Sciencesul. Newelska 601-447 WarsawPolandE-mail: kacprzyk@ibspan.waw.plFurther volumes of this series can be found on ourhomepage: springer.comVol. 94. Arpad Kelemen, Ajith Abraham and Yuehui Chen(Eds.)Computational Intelligence in Bioinformatics, 2008ISBN 978-3-540-76802-9Vol. 95. Radu DogaruSystematic Design for Emergence in Cellular NonlinearNetworks, 2008ISBN 978-3-540-76800-5Vol. 96. Aboul-Ella Hassanien, Ajith Abraham and JanuszKacprzyk (Eds.)Computational Intelligence in Multimedia Processing:Recent Advances, 2008ISBN 978-3-540-76826-5Vol. 97. Gloria Phillips-Wren, Nikhil Ichalkaranje andLakhmi C. Jain (Eds.)Intelligent Decision Making: An AI-Based Approach, 2008ISBN 978-3-540-76829-9Vol. 98. Ashish Ghosh, Satchidananda Dehuri and SusmitaGhosh (Eds.)Multi-Objective Evolutionary Algorithms for KnowledgeDiscovery from Databases, 2008ISBN 978-3-540-77466-2Vol. 99. George Meghabghab and Abraham KandelSearch Engines, Link Analysis, and Users Web Behavior,2008ISBN 978-3-540-77468-6Vol. 100. Anthony Brabazon and Michael ONeill (Eds.)Natural Computing in Computational Finance, 2008ISBN 978-3-540-77476-1Vol. 101. Michael Granitzer, Mathias Lux and Marc Spaniol(Eds.)Multimedia Semantics - The Role of Metadata, 2008ISBN 978-3-540-77472-3Vol. 102. Carlos Cotta, Simeon Reich, Robert Schaefer andAntoni Ligeza (Eds.)Knowledge-Driven Computing, 2008ISBN 978-3-540-77474-7Vol. 103. Devendra K. ChaturvediSoft Computing Techniques and its Applications in ElectricalEngineering, 2008ISBN 978-3-540-77480-8Vol. 104. Maria Virvou and Lakhmi C. Jain (Eds.)Intelligent Interactive Systems in Knowledge-BasedEnvironment, 2008ISBN 978-3-540-77470-9Vol. 105. Wolfgang GuenthnerEnhancing Cognitive Assistance Systems with InertialMeasurement Units, 2008ISBN 978-3-540-76996-5Vol. 106. Jacqueline Jarvis, Dennis Jarvis, Ralph R onnquistand Lakhmi C. Jain (Eds.)Holonic Execution: A BDI Approach, 2008ISBN 978-3-540-77478-5Vol. 107. Margarita Sordo, Sachin Vaidya and Lakhmi C. Jain(Eds.)Advanced Computational Intelligence Paradigmsin Healthcare - 3, 2008ISBN 978-3-540-77661-1Vol. 108. Vito TrianniEvolutionary Swarm Robotics, 2008ISBN 978-3-540-77611-6Vol. 109. Panagiotis Chountas, Ilias Petrounias and JanuszKacprzyk (Eds.)Intelligent Techniques and Tools for Novel SystemArchitectures, 2008ISBN 978-3-540-77621-5Vol. 110. Makoto Yokoo, Takayuki Ito, Minjie Zhang,Juhnyoung Lee and Tokuro Matsuo (Eds.)Electronic Commerce, 2008ISBN 978-3-540-77808-0Vol. 111. David Elmakias (Ed.)New Computational Methods in Power System Reliability,2008ISBN 978-3-540-77810-3Vol. 112. Edgar N. Sanchez, Alma Y. Alans and AlexanderG. LoukianovDiscrete-Time High Order Neural Control: Trained withKalman Filtering, 2008ISBN 978-3-540-78288-9Vol. 113. Gemma Bel-Enguix, M. Dolores Jimenez-Lopez andCarlos Martn-Vide (Eds.)New Developments in Formal Languages and Applications,2008ISBN 978-3-540-78290-2Vol. 114. Christian Blum, Maria Jos e Blesa Aguilera, AndreaRoli and Michael Sampels (Eds.)Hybrid Metaheuristics, 2008ISBN 978-3-540-78294-0Vol. 115. John Fulcher and Lakhmi C. Jain (Eds.)Computational Intelligence: A Compendium, 2008ISBN 978-3-540-78292-6John FulcherLakhmi C. Jain(Eds.)Computational Intelligence:A CompendiumWith 321 Figures and 67 Tables123Prof. John FulcherSchool of Computer Scienceand Software EngineeringFaculty of InformaticsUniversity of WollongongNorthelds AveWollongong NSW 2522Australiajohn@uow.edu.auProf. L.C. JainKnowledge-Based EngineeringFounding Director of the KES CentreSCT-BuildingMawson Lakes CampusUniversity of South AustraliaAdelaide South Australia SA 5095AustraliaLakhmi.jain@unisa.edu.auISBN 978-3-540-78292-6 e-ISBN 978-3-540-78293-3Studies in Computational Intelligence ISSN 1860-949XLibrary of Congress Control Number: 2008922060c 2008 Springer-Verlag Berlin HeidelbergThis work is subject to copyright. All rights are reserved, whether the whole or part of the material is con-cerned, specically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microlm or in any other way, and storage in data banks. Duplication of this publica-tion or parts thereof is permitted only under the provisions of the German Copyright Law of September9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag.Violations are liable to prosecution under the German Copyright Law.The use of general descriptive names, registered names, trademarks, etc. in this publication does notimply, even in the absence of a specic statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.Cover design: Deblik, Berlin, GermanyPrinted on acid-free paper9 8 7 6 5 4 3 2 1springer.comDedicated to the creative spark within us allPrefaceAt this point in time, Computational Intelligence (CI) has yet to matureas a discipline in its own right. Accordingly, there is little consensus as toa precise denition of this emerging eld. Nevertheless, most practitionerswould include Articial Neural Network (ANN), Fuzzy and evolutionary tech-niques (and perhaps others), and more especially hybrids of these (this willbe expanded upon in Chap. 1.) Our emphasis in this Compendium is verymuch on applied methods ones which have been tired-and-proven eectiveon real-world problems.The 25 chapters have been grouped into the following ten themes (Parts):I. Overview, BackgroundII. Data Preprocessing, Systems Integration & VisualizationIII. Articial IntelligenceIV. Logic and ReasoningV. OntologyVI. Intelligent AgentsVII. Fuzzy SystemsVIII. Articial Neural NetworksIX. Evolutionary ApproachesX. DNA and Immunity-based ComputingThis grouping is not the only one we could have used indeed somechapters could have just as easily appeared in alternate Parts of the Hand-book. For example, Lam & Lees iJADE tourist guidance system could justas readily been grouped into Part-VI (Agents) as Part-V (Ontology); like-wise, Fyfes Topographic Maps would have tted just as well into Part-VIII(ANNs), Ishibuchi et al. would have been just as equally well placed in eitherPart-VII or Part-IX, and Islam & Yao could have tted equally well intoParts VIII or IX. Nevertheless, we have attempted to group together chapterswith common foci.Returning briey to the question of real-world applications, Table 1summarizes those covered in the chapters herein.VIII PrefaceTable 1. Chapter methods and applicationsChapter Method Application(s) Data set(s)2 REDR; SOM; handwritten digits; UCI KDDMitra Multi-Scale yeast; synthetic3 SOM; GTM; data visualization UCI-ML (algae; wine)HaToM; ToPE4 networked selsh self-maintenance/repair; Denial-Of-Serviceagents; probabilistic internet being & Traceroutecellular automata websites5 aective embodied synthetic therapist; agent human behaviour change6 paraconsistent pipeline safety process annotated logic verication7 Data-Oriented Natural Language Processing; Penn Treebank;Parsing musical notation, physics Essen Folksongproblem solving Collection8 Conceptual Graph ontologies: architectural Theory design; air operations ocer9 mobile/GPS agents; tourist guidance system DAML; Protegeontologies10 Agent-Based Stupid model Santa Fe InstituteModelling software articial stock market11 agents; Peer-to-Peer resource allocation; & cluster computing communication; scheduling12 multi-agents; sensor networks dynamic clustering13 agents; ANN; SVM; computational economics; SFI and AI-ECONEvolutionary Comp. foreign exchange rates articial stock markets14 reconciliation via fuzzy rule-based systems optimization15 Evolutionary fuzzy rule-based classiers UCI-ML (breast; glass;multi-objective design heart; iris; wine)16 ANNs; MAS network bandwidth prediction17 SOM; ViSOM; vector quantization; UCI-ML (iris; yeast)SOMN; kernel image compression/methods segmentation; text mining18 neural systems Blue Brain; SPINN; engineering SpiNNaker19 GenNt; CGA; FPGA articial brain; UXO robot 20 Evolutionary ANN Credit Card; Diabetes; UCI-MLensembles Heart Disease; Glass; Letter;Soybean; Breast Cancer21 genetic simulated graph colouring; bin DIMACS; Scholl &annealing packing; timetabling Klein; Falkenauer22 Genetic modeling; the Humies; Programming (GP) image/signal processing;time series prediction; et al.Preface IXTable 1. (continued)Chapter Method Application(s) Data set(s)23 Particle Swarm nding origin; Rastrigin/ Optimization Schwefel functions;timetabling24 DNA computing multiple elevator scheduling 25 immunity-based stable marriage problem; computing auto sensor diagnosis; noiseneutralizationChapters commence with an overview of the eld in question, and concludewith a Resources Appendix. These resources cover key references (classic texts,survey articles, key papers), pertinent journals, professional societies/organi-zations and research groups, international conferences and workshops, and/orelectronic/on-line material (with a particular emphasis on databases and OpenSource software). Each chapter is thus complete unto itself for those readerswanting to explore just a single topic from the many on oer. In this mode,the Compendium could be used as a text for graduate programs in ArticialIntelligence, Intelligent Systems, Soft Computing, Computational Intelligence,and the like. The Index doubles as a Glossary of Terms (with acronyms shownin parentheses).We have gathered together in a single volume chapters by leading expertsin their respective elds all of international repute, as evidenced (in part)by the following:I. Learned Society Fellows: IEEE (Furber, Pedrycz, Yao); Intl. Fuzzy Sys-tems Association (Pedrycz); Royal Society (Furber); Royal Academy ofEngineering (Furber); British Computer Society (Furber); Intl. Soci-ety for Genetic and Evolutionary Computation (Koza, Langdon, Poli);Institution of Engineers, Australia (Jain)II. Editors-in-Chief: Computing and Information Systems (Fyfe); IEEETrans. Evolutionary Computing (Yao); Information Sciences (Pedrycz);Intelligent Decision Technologies (Jain); Intl. J. Hybrid Intelligent Sys-tems (Jain); Intl. J. Knowledge-based Intelligent Engineering Systems(Founding Editor) (Jain); Intl. J. Logic & Reasoning (Nakamatsu); Intl.J. Metaheuristics (Mumford); New Mathematics & Natural Computing(Chen)III. Associate/Area Editors: Advances in Natural Computation (Yao); Com-puter and Information Systems (Jain); Evolutionary Computation (Poli);IEEE Computational Intelligence Magazine (Ishibuchi); IEEE Trans.Evolutionary Computation (Ishibuchi); IEEE Trans. Fuzzy Systems(Ishibuchi, Pedrycz); IEEE Trans. Knowledge and Data Engineer-ing (Wang); IEEE Trans. Neural Networks (Pedrycz, Wang); IEEETrans. Neural Networks (Chen, Wang); IEEE Trans. Systems, Man andX PrefaceCybernetics (Ishibuchi, Jain, Pedrycz); Intl. J. Advances in Fuzzy Systems(Ishibuchi); Intl. J. Applied Intelligence (Hendtlass); Intl. J. Computa-tional Intelligence Research (Ishibuchi, Poli); Intl. J. Information Tech-nology (Chow); Intl. J. Knowledge-based Intelligent Engineering Systems(Ishida); Intl. J. Metaheuristics (Ishibuchi); Intl. J. Pattern Recognitionand Articial Intelligence (Jain); J. Articial Evolution and Applica-tions (McPhee); J. Economic Research (Chen); J. Genetic Program-ming and Evolvable Machines (Poli); J. Intelligent and Fuzzy Systems(Jain); Mathware and Soft Computing (Ishibuchi); Neural Computing andApplications (Jain); Soft Computing J. (Ishibuchi)IV. Book Series Editors: CRC Press (Intl. series on CI) (Jain); IGI (CI: The-ory and Applications series) (Fyfe, Jain); Kluwer (Genetic Programming)(Koza); Springer (Advanced Information and Knowledge Processing)(Jain)V. Invited Keynote/Plenary Conference Speakers: Chen, deGaris, Fulcher,Fyfe, Ishida, Jain, Koza, Nakamatsu, Prokopenko, Wang, YaoVI. Awards: Royal Society Wolfson Research Merit Award (Furber); IETFaraday Medal (Furber); Queens Award for Technology (Furber);Japanese Society for the Promotion of Science Prize (Ishibuchi); IEEEDonald G. Fink Prize Paper (Yao); AJB CEBIT Exhbition Award (MostInnovative Technology) (Prokopenko); Japanese Society for ArticialIntelligence Award (Prokopenko); Best Paper Awards (Chow, Langdon,McPhee, Poli)VII. Advisory Boards: UK EPSRC Peer Review College (Poli); EU ExpertEvaluator (Poli)Indeed, this Handbook is a truly international undertaking, with a total of 43authors from 10 dierent countries contributing (9 in Australia; 19 in Asia; 11in UK; and 4 in North America). All chapters were peer reviewed to ensure auniformly high standard for the Handbook overall.Next follows an overview of each Chapter.In Part-I, Fulcher introduces fundamental Computational Intelligence con-cepts. This Introductory chapter is intended to serve both as backgroundreading (tutorial) for students/newcomers to the eld, as well as setting thescene/laying the groundwork for the more specialist chapters that follow.A critical consideration with applying any CI approach to real-world prob-lems is data pre-processing. Part-II begins with a chapter by Chow & Huangon Data Reduction for Pattern Recognition. They provide an overview of l-ter and wrapper methods, before focusing on their Representative EntropyData Reduction (REDR) approach. In their discussion they cover not onlydata reduction but also feature selection another key aspect of pre-processing.Fyfes concern in Chap. 3 is data visualization. He illustrates how theSelf-Organizing Map, Generative and Harmonic Topographic Maps, and thePreface XITopographic Product-of-Experts can all be eectively used on the UCI-MLalgae and wine data sets.Ishida adopts a game-theoretic approach to self-maintenance and repair inlarge-scale, complex systems such as the Internet more specically, selshagents and Probabilistic Cellular Automata. Emphasis is placed on the emer-gence of intelligent systems at the Nash Equilibrium in such networks. Heconcludes with speculation as to the conditions necessary for the emergenceof a so-called Internet Being, in the context of such selshware.Part-III covers conventional (traditional) AI, but from a slightly dierentperspective. In Chap. 5 Creed and Beale provide a fascinating insight intoEmotional Intelligence and aective computing. They commence with a dis-cussion of emotion theory, before introducing aective embodied agents. Theauthors then demonstrate how such agents can be applied in the real world toengender behavioural change in humans, through the medium of a so-calledsimulated (articial) therapist.Part-IV covers logic, reasoning and related approaches to CI. In Chap. 6Nakamatsu provides a comprehensive discussion of various paraconsistentannotated logics, including his own extended vector-annotated logic pro-gram with strong negation EVALPSN; defeasible reasoning proofs are alsoincluded. Nakamatsu then proceeds to show how EVALPSN can be applied topipeline process safety verication, and by extension to pipeline process ordersafety verication (by way of before-after EVALPSN).Bod uses the supervised, corpus-based probabilistic Data-Oriented Pars-ing approach to reveal the underlying structures in areas as diverse as NaturalLanguage Processing, the melodic analysis of musical scores, and physics prob-lem solving. A mechanismfor determining the optimum parse tree is described,and the chapter concludes with an explanation of how DOP could be modiedto support unsupervised learning.Part-V of the Handbook covers ontology, which is often closely relatedto intelligent agents (Part-VI). In Chap. 8, Corbett takes the view thatframeworks for intelligent systems are best represented by a combination ofconcept type hierarchy, canonical formation rules, conformity relations andsubsumption. He illustrates the validity of this approach with regard to botharchitectural design and air operation ocer ontologies, and concludes thathis automated reasoning approach could be readily extended to the SemanticWeb.In Chap. 10, Lam and co-authors provide an account of an IntelligentOntology Agent-based Tourist Guidance System, which they developed usingthe Intelligent Java Agent-based Development Environment (iJADE).Part-VI is devoted to (intelligent) software agents. Standish commenceswith a comparative review in Chap. 11 of open source Agent-based ModellingXII Prefaceplatforms, including a performance comparison on a simple pedagogical model(the so-called Stupid Model).Zhang and co-authors follow in Chap. 11 with a discussion of their agent-based SmartGRID model incorporating both Peer-to-Peer and clusteringtechniques and which can yield improved resource allocation, as well astask communication and scheduling in agent grids operating in open envi-ronments. Piraveenan and co-authors are likewise interested in grids, but intheir case scale-free sensor networks. They use a dynamic, decentralized MASalgorithm for predicting convergence times for cluster formation in such grids(networks).Chen provides a comprehensive account of agent-based computational eco-nomics (ACE)in Chap. 13, with a particular focus on Genetic Algorithms.After introducing the cobweb and overlapping generations models, he describesseveral applications of ACE, including ination, foreign exchange rate, andarticial stock markets.Fuzzy Systems are the focus of Part-VII. Pedrycz commences with a dis-cussion of the semantics and perception of fuzzy sets and fuzzy mapping. Heproceeds to show that reconciliation of fuzzy set perception and granular map-pings can be expressed in the form of an optimization pattern, which in turncan be subsequently applied to rule-based systems.In Chap. 15, Ishibushi and co-authors discuss the principles underlying theevolutionary design of fuzzy classiers, illustrating the eectiveness of theirapproach by way of data sets selected from the UCI-ML repository (breastcancer; glass; heart; iris; wine).Part-VIII covers Articial Neural Networks. Fu and co-authors commenceby illustrating how supervised, feedforward networks (MLP/BP) can be usedin the data mining of Quality-of-Service aware media grids.Yin provides a comprehensive coverage of Kohonens Self-Organizing Mapand its more recent variants in Chap. 17. The performance of SOM, ViSOM,SOM Mixture Network and SOM kernel methods is compared on vectorquantization, image compression and segmentation, density modelling, datavisualization and text mining.In Chap. 18, Furber & Temple provide an overview of neural systems engi-neering, namely the realization of ANNs in hardware form, rather than themore usual approach of software simulation. DeGaris follows in a similar vein,describing how Field Programmable Gate Arrays (FPGAs) can be used tobuild articial brains.Evolutionary approaches to CI are the subject of Part-IX of the Handbook.Islam & Yao commence with an account of how ANN ensembles can be rstevolved, then used as classiers on representative data sets from the UCI-MLrepository.Preface XIIIIn Chap. 21 Mumford concerns herself with a so-called memetic EA inthe form of a Genetic Simulated Algorithm (GSA) which she proceeds todemonstrate can be used to solve set partitioning problems (graph colouring,bin packing, timetabling).Genetic Programming is the focus of the Chapter by Langdon and co-authors. An extensive coverage of basic principles is followed by descriptionsof how to apply GPs in various application domains, including hints for thenovice user (tricks-of-the-trade, as it were). The Chapter rounds o with asection on theoretical aspects of GP. The authors include an extensive (420item) reference list.In Chap. 23, after providing an overview of the basic Particle Swarm Opti-mization algorithm, Hendtlass then proceeds to describe enhancements tohandle multiple optima, niching and Waves of Swarm Particles, before out-lining how one can minimize the computational cost of such algorithms. Heillustrates the eectiveness of the PSO approach by way of nding the origin,solving Rostrigins and Schwefels functions, and timetabling.In the last Part of the Handbook we cover CI approaches inspired byNature, but which do not fall under the umbrella of ANNs, EAs or Fuzzy.Firstly, Watada provides an overview of DNA Computing, then proceeds toshow how this can be successfully applied to the problem of scheduling themovements of a group of elevators in a high-rise building.Chapter 25 is concerned with Immunity-based computing (IBC). Afterintroducing the basic concepts, Ishida shows how IBC can be applied toautomobile sensor diagnosis, noise neutralization, and the Stable MarriageProblem. He concludes by proposing a general (immunity-based) problemsolver.One of your Editors (JF) formatted the Handbook, using MikTex v2.4 andWinEdit v5.4. In this (considerable) endeavour we are indebted to the follow-ing for their assistance along the way: Professor Philip Ogunbona (for impart-ing the joy of LaTex), Associate Professor Willy Susilo, Associate ProfessorRussell Standish, Professor Riccardo Poli, Dr. Bill Langdon, and Jia Tang forinsight into the ner points of LaTex, as well as Nik Milosevic (for creation of.eps gures). Thanks are also due to Dr. Thomas Ditzinger, Senior Editor, andHeather King, Engineering Editorial, respectively at Springer-Verlag GmbH,as well as Srilatha Achuthan, Project Manager at SPi Technologies, Chennai.We sincerely trust that you nd much of interest in the ensuing pages.Wollongong NSW, Australia John FulcherAdelaide SA, Australia Lakhmi C. JainSeptember 2007ContentsPart I Overview, BackgroundComputational Intelligence: An IntroductionJohn Fulcher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Introduction, Overview, Denitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Historical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1 Articial Intelligence (AI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Machine Learning (ML) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Approaches to CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1 The Intuitive Appeal of Nature . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Brains versus Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 CI Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Articial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267.1 ANN Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267.2 Multi-Layer Perceptron/BackPropagation . . . . . . . . . . . . . . . . 277.3 Other ANN Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Evolutionary Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318.1 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328.2 Evolutionary Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368.3 Genetic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368.4 Swarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Immunity-Based and Membrane-Based Computing . . . . . . . . . . . . . . 399.1 Immunity-Based Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.2 Membrane-Based Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . 4010 DNA Computing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4011 Intelligent Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41XVI Contents12 Hybrid Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671.1 Computational Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671.2 Articial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681.3 Evolutionary Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691.4 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711.5 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722.1 Articial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722.2 Evolutionary Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732.3 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732.4 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733 Organizations, Societies, Special Interest Groups, Journals . . . . . . . 743.1 Computational Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.2 Articial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.3 Evolutionary Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.4 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.5 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 765 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 776 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Part II Preprocessing, Visualization, Systems IntegrationData Reduction for Pattern Recognition and DataAnalysisTommy W.S. Chow and Di Huang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822.1 Wrapper Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832.2 Filter Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832.3 Examples of Filter Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893.1 Feature Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913.2 Search Engine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973.3 Example Feature Selection Models . . . . . . . . . . . . . . . . . . . . . . . 984 Trends and Challenges of Feature Selection and Data Reduction . . 101References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Contents XVIIResources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1071 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 Organizations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 1084 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1085 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1086 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 1087 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1098 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Topographic Maps for Clustering and Data VisualizationColin Fyfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1112 Clustering and Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1123 The Self-Organizing Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1133.1 Competitive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1133.2 Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173.3 Alternative Traditional Topology Preserving Mappings . . . . . 1183.4 A Last Word. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1204 The Generative Topographic Mapping . . . . . . . . . . . . . . . . . . . . . . . . . 1214.1 Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224.2 Adjusting the Latent Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244.3 Deleting Latent Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265 Topographic Product of Experts (ToPoE) . . . . . . . . . . . . . . . . . . . . . . 1265.1 Comparison with the GTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1295.2 Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305.3 Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315.4 Growing and Pruning ToPoEs . . . . . . . . . . . . . . . . . . . . . . . . . . . 1335.5 Dierent Noise Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1355.6 Twinned ToPoEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1355.7 Visualizing and Clustering Real Data Sets . . . . . . . . . . . . . . . . 1365.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1396 Harmonic Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1406.1 Harmonic k-means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1416.2 The Harmonic Topographic Map . . . . . . . . . . . . . . . . . . . . . . . . 1426.3 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1426.4 Generalized Harmony Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 1446.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1467 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1511 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1512 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1513 Key Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152XVIII Contents4 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 1525 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1536 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Complex Systems Paradigms for Integrating IntelligentSystems: A Game Theoretic ApproachYoshiteru Ishida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1551 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1552 Economic Theory for the Internet Being with Selsh Agents . . . . . . 1573 A Microscopic Model: Negotiation Between Agents . . . . . . . . . . . . . . 1593.1 The Prisoners Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1593.2 Repairing from Outside the System:A Conventional Model [12] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1603.3 Mutual Repair within Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 1603.4 Mutual Repair with Selsh Agents . . . . . . . . . . . . . . . . . . . . . . . 1614 A Macroscopic Model: Boundary Formation among Agents . . . . . . . 1634.1 A Model with Uniform Control . . . . . . . . . . . . . . . . . . . . . . . . . . 1634.2 The Spatial Prisoners Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . 1674.3 A Model with Selsh Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1684.4 Strategic Repair with Systemic Payo . . . . . . . . . . . . . . . . . . . . 1694.5 Comparison Between Uniform Repairand Strategic Repair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1705 Selshware and Internet Being . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1736 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1791 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1792 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 1793 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1804 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1805 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 1806 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1817 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Part III Articial IntelligenceEmotional Intelligence: Giving Computers EectiveEmotional Skills to Aid InteractionChris Creed and Russell Beale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1851 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1852 Overview of Aective Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1872.1 What Are Emotions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1872.2 Emotions and Moods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1902.3 Expression of Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191Contents XIX2.4 Inuence of Emotion on Human behavior . . . . . . . . . . . . . . . . . 1932.5 Emotional Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1962.6 Approaches Used in Developing EmotionallyIntelligent Computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1972.7 Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2033 Evaluating Aective Embodied Agents . . . . . . . . . . . . . . . . . . . . . . . . . 2063.1 What are Aective Embodied Agents? . . . . . . . . . . . . . . . . . . . 2073.2 Psychological Responses to Simulated Emotion . . . . . . . . . . . . 2073.3 Evaluating Agents over Extended Interactions . . . . . . . . . . . . . 2093.4 Our Aective Embodied Agent . . . . . . . . . . . . . . . . . . . . . . . . . . 2104 Application of Aective Embodied Agents . . . . . . . . . . . . . . . . . . . . . . 2114.1 Aective Embodied Agents for behavior Change . . . . . . . . . . . 2124.2 Behavior Change Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2135 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2251 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2252 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2263 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 2264 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2275 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2286 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 2287 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.1 Multimodal Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.2 Face Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230Part IV Logic and ReasoningThe Paraconsistent Annotated Logic Program EVALPSNand its ApplicationKazumi Nakamatsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2331 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2331.1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2331.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2342 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2352.1 Paraconsistent Annotated Logics PT . . . . . . . . . . . . . . . . . . . . 2352.2 Generally Horn Program(GHP) . . . . . . . . . . . . . . . . . . . . . . . . . 2372.3 ALPSN (Annotated Logic Program with StrongNegation) and Stable Model Semantics . . . . . . . . . . . . . . . . . . . 2412.4 VALPSN (Vector Annotated Logic Program withStrong Negation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243XX Contents2.5 EVALPSN (Extended Vector Annotated LogicProgram with Strong Negation) and DefeasibleDeontic Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2442.6 Defeasible Reasoning and VALPSN . . . . . . . . . . . . . . . . . . . . . . 2472.7 Defeasible Deontic Reasoning and EVALPSN . . . . . . . . . . . . . 2563 EVALPSN Safety Verication for Control . . . . . . . . . . . . . . . . . . . . . . 2653.1 Outline of EVALPSN Safety Verication . . . . . . . . . . . . . . . . . 2653.2 EVALPSN Safety Verication for Pipeline Control . . . . . . . . . 2664 Before-after EVALPSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2844.1 Before-after Relation in EVALPSN . . . . . . . . . . . . . . . . . . . . . . 2854.2 Implementation of bf-EVALPSN . . . . . . . . . . . . . . . . . . . . . . . . 2914.3 Safety Verication in bf-EVALPSN . . . . . . . . . . . . . . . . . . . . . . 2955 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3051 Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3052 Paraconsistent Annotated Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3053 Defeasible Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3054 Defeasible Deontic Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3065 ALPSN, VALPSN, EVALPSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3066 EVALPSN Safety Verication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306The Data-Oriented Parsing Approach:Theory and ApplicationRens Bod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3071 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3072 A DOP Model for Language: Combining Likelihoodand Simplicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3083 A DOP Model for Music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3154 A DOP Model for Problem Solving in Physics . . . . . . . . . . . . . . . . . . 3185 Towards a Unifying Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3236 Test Corpora for DOP+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3257 Computing Tbest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3278 Experiments with DOP+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3309 Current Developments: Unsupervised DOP . . . . . . . . . . . . . . . . . . . . . 33310 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3431 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3432 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3433 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 3444 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3455 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Contents XXI6 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 3467 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3478 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3478.1 Multimodal Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3478.2 Face Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348Part V OntologyGraph-Based Representation and Reasoningfor OntologiesDan R. Corbett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3511 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3512 Overview of Conceptual Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3522.1 The Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3522.2 Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3542.3 Canonical Formation Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3552.4 Types and Inheritance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3552.5 Specialization, Projection and Subsumption. . . . . . . . . . . . . . . 3573 Projection as an Ontology Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . 3584 Projection of Ontology Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3605 Knowledge Conjunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3625.1 Ontology Comparison and Conjunction. . . . . . . . . . . . . . . . . . . 3625.2 Unication, Constraints and Conceptual Graphs . . . . . . . . . . . 3645.3 Knowledge Structures, Partialness and Unication . . . . . . . . . 3656 An Architectural Design Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3677 An Architectural Design Tool: Results and Discussion . . . . . . . . . . . 3688 The Air Operations Ocer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3709 The Air Operations Ocer: Results and Discussion. . . . . . . . . . . . . . 37210 Conclusions: Semantics for a Knowledge Web . . . . . . . . . . . . . . . . . . . 372References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3771 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3772 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3773 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3784 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3785 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 3786 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379An Ontology-Based Intelligent Mobile System for TouristGuidanceToby H.W. Lam, Raymond S.T. Lee, and James N.K. Liu . . . . . . . . . . . 3811 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3812 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3832.1 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3832.2 Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386XXII Contents3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3884 Ontology-Based Tourist Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3894.1 iJADE Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3894.2 Construction of the Travel Ontology . . . . . . . . . . . . . . . . . . . . . 3904.3 iJADE FreeWalker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3954.4 iJADE System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3975 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4005.1 Precision Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4015.2 Usability Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4016 Conclusion and Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4051 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4052 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4053 Websites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4054 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 4065 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4066 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406Part VI Intelligent AgentsOpen Source Agent-Based Modeling FrameworksRussell K. Standish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4091 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4091.1 Articial Life (Alife) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4092 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4112.1 Sugarscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4122.2 The Santa Fe Articial Stock Market . . . . . . . . . . . . . . . . . . . . 4132.3 Heatbugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4162.4 Mousetrap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4173 Software Modeling Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4173.1 Open Source versus Freeware . . . . . . . . . . . . . . . . . . . . . . . . . . . 4173.2 Programming Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4183.3 Reection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213.4 User Interface and Scripting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213.5 Discrete Event Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4223.6 Random Number Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4223.7 Swarm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4233.8 Repast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4243.9 Mason . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4253.10 EcoLab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4263.11 The Logos, StarLogo and NetLogo . . . . . . . . . . . . . . . . . . . . . . . 4273.12 Cormas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428Contents XXIII4 Performance Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4285 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4351 ABM Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4352 Discussion Fora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436Agent-Based Grid ComputingMinjie Zhang, Jia Tang, and John Fulcher . . . . . . . . . . . . . . . . . . . . . . . . . 4391 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4392 Computing Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4402.1 Development of Computing Grids . . . . . . . . . . . . . . . . . . . . . . . 4412.2 Application-Oriented Metacomputing . . . . . . . . . . . . . . . . . . . . 4422.3 Service-Oriented Grid Computing . . . . . . . . . . . . . . . . . . . . . . . 4432.4 Convergence of Grids and Peer-to-Peer Computing . . . . . . . . 4442.5 Research Questions of Grid Computing. . . . . . . . . . . . . . . . . . . 4453 Grid Computing in Open Environments . . . . . . . . . . . . . . . . . . . . . . . . 4464 SmartGrid A Hybrid Solution to Grid Computingin Open Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4464.1 Overall Architecture and Core Components . . . . . . . . . . . . . . . 4474.2 The Task/Service Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4514.3 The smartGRID Scheduling Process . . . . . . . . . . . . . . . . . . . . . 4535 A Peer-to-Peer Solution to Grid Computingin Open Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4625.1 Overall Architecture and Core Componentsof smartGRID2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4625.2 Module An Improved Task Model . . . . . . . . . . . . . . . . . . . . . . 4655.3 Peer-to-Peer Computing Architecture . . . . . . . . . . . . . . . . . . . . 4675.4 Resource Management and Scheduling Mechanisms . . . . . . . . 4715.5 Compatibility and Inter-Operability . . . . . . . . . . . . . . . . . . . . . 4756 Conclusion and Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4811 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4812 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4813 Journal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4824 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 4825 Web Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482Decentralized Multi-Agent Clustering in Scale-freeSensor NetworksMahendra Piraveenan, Mikhail Prokopenko, Peter Wang,and Astrid Zeman. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4851 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485XXIV Contents1.1 Multi-Agent Systems and Self-organization . . . . . . . . . . . . . . . 4851.2 Multi-Agent Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4871.3 Adaptive Topologies and Dynamic Hierarchies . . . . . . . . . . . . 4882 Dynamic Cluster Formation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 4903 Regularity of Multi-Agent Communication-Volume . . . . . . . . . . . . . . 4944 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4965 An Application Scenario Distributed Energy Managementand Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5016 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Decentralised Clustering Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . 507Predictor K2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5131 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5132 Key Survey/Review Article . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5133 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 5134 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5145 Discussion Group, Forum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5146 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 514Computational Intelligence in Agent-BasedComputational EconomicsShu-Heng Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5171 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5171.1 What is Agent-Based Computational Economics (ACE)? . . . 5171.2 Algorithmic Foundations of ACE . . . . . . . . . . . . . . . . . . . . . . . . 5182 Articial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5192.1 Multilayer Perceptron Neural Networks . . . . . . . . . . . . . . . . . . 5202.2 Radial Basis Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5222.3 Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5222.4 Auto-Associative Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 5242.5 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5282.6 Self-Organizing Maps and k-means . . . . . . . . . . . . . . . . . . . . . . 5292.7 K Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5322.8 Instance-Based Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5333 Evolutionary Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5363.1 Evolutionary Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5383.2 Evolutionary Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5403.3 Genetic Programming and Genetic Algorithms . . . . . . . . . . . . 5404 Agent-Based Economic Simulations with CI . . . . . . . . . . . . . . . . . . . . 5494.1 The Cobweb Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5494.2 Overlapping Generations Models . . . . . . . . . . . . . . . . . . . . . . . . 552Contents XXV4.3 Foreign Exchange Rate Fluctuations . . . . . . . . . . . . . . . . . . . . . 5584.4 Articial Stock Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5624.5 Market/Policy Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5685 Pushing the Research Frontier with CI . . . . . . . . . . . . . . . . . . . . . . . . . 5705.1 Developments in Agent Engineering. . . . . . . . . . . . . . . . . . . . . . 5705.2 Distinguishing Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5725.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5766 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5911 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5912 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5923 Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5924 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 5924.1 Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5924.2 Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5935 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5936 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594Part VII Fuzzy SystemsSemantics and Perception of Fuzzy Setsand Fuzzy MappingsWitold Pedrycz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5971 Semantics of Fuzzy Sets: Some General Observations . . . . . . . . . . . . 5972 Domain Knowledge and Problem-Oriented Formationof Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5992.1 Fuzzy Set as a Descriptor of Feasible Solutions . . . . . . . . . . . . 5992.2 Fuzzy set as a Descriptor of the Notion of Typicality . . . . . . . 6012.3 Membership Functions in the Visualizationof Preferences of Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6033 User-Centric Estimation of Membership Functions . . . . . . . . . . . . . . 6053.1 Horizontal Membership Function Estimation Scheme . . . . . . . 6053.2 Vertical Membership Function Estimation Scheme . . . . . . . . . 6063.3 Pairwise Membership Function Estimation Scheme . . . . . . . . 6074 Fuzzy Sets as Granular Representatives of Numeric Data. . . . . . . . . 6095 From Multidimensional Numeric Data to Fuzzy Sets:Membership Estimation via Fuzzy Clustering . . . . . . . . . . . . . . . . . . . 6146 Main Design Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6207 Nonlinear Transformation of Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . 6218 Reconciliation of Information Granule Perception . . . . . . . . . . . . . . . 6259 The Optimization Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626XXVI Contents10 An Application of the Perception Mechanismto Rule-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62711 Reconciliation of Granular Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . 62912 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6371 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6372 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6373 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 6384 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6385 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 639Evolutionary Multiobjective Designof Fuzzy Rule-Based ClassiersHisao Ishibuchi, Yusuke Nojima, and Isao Kuwajima . . . . . . . . . . . . . . . . 6411 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6412 Fuzzy Rule-Based Classiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6442.1 Pattern Classication Problems . . . . . . . . . . . . . . . . . . . . . . . . . 6442.2 Fuzzy Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6442.3 Fuzzy Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6482.4 Fuzzy Rule Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6502.5 Comparison Between Fuzzy and Interval Rules . . . . . . . . . . . . 6533 Evolutionary Multiobjective Optimization (EMO) . . . . . . . . . . . . . . . 6553.1 Genetic Algorithms (GAs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6553.2 Multiobjective Optimization (MO) . . . . . . . . . . . . . . . . . . . . . . 6573.3 Evolutionary Multiobjective Optimization (EMO) . . . . . . . . . 6584 Two Approaches to Evolutionary Multiobjective Designof Fuzzy Rule-Based Classiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6614.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6624.2 Multiobjective Fuzzy Rule Selection . . . . . . . . . . . . . . . . . . . . . 6634.3 Multiobjective Fuzzy Genetics-Based Machine Learning . . . . 6674.4 Computational Experiments on Test Problems . . . . . . . . . . . . 6695 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6736 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6811 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6811.1 Fuzzy Rule-Based Classication Systems . . . . . . . . . . . . . . . . . 6811.2 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6811.3 Genetic Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6821.4 Evolutionary Multiobjective Optimization . . . . . . . . . . . . . . . . 6821.5 Evolutionary Multiobjective Machine Learningand Knowledge Extraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682Contents XXVII2 Conferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6822.1 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6822.2 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6832.3 Genetic Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6832.4 Evolutionary Multiobjective Optimization . . . . . . . . . . . . . . . . 6832.5 Hybrid Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6832.6 Broader Areas, Including Fuzzy Systemsand Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6833 Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6833.1 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6833.2 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6843.3 Broader Areas, Including Fuzzy Systemsand Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6844 Websites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6845 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6856 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685Part VIII Articial Neural NetworksData Mining in QoS-Aware Media GridsXiuju Fu, Xiaorong Li, Lipo Wang, David Ong,and Stephen John Turner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6891 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6892 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6912.1 Network Bandwidth Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . 6912.2 Brief Overviews on Neural Networks . . . . . . . . . . . . . . . . . . . . . 6923 System Model of Data Analysis over Media Grid . . . . . . . . . . . . . . . . 6943.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6943.2 System Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6964 Data Mining Strategy for Bandwidth Prediction . . . . . . . . . . . . . . . . 6974.1 Multi-Layer Perceptron Neural Network . . . . . . . . . . . . . . . . . . 6974.2 Data Mining Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6994.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7015 Experimental System and Performance Evaluation . . . . . . . . . . . . . . 7025.1 System Hardware and Software . . . . . . . . . . . . . . . . . . . . . . . . . 7025.2 Request Arrival Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7025.3 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7036 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7131 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7132 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7133 Organisations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 714XXVIII Contents4 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 7145 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714The Self-Organizing Maps: Background, Theories,Extensions and ApplicationsHujun Yin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7151 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7152 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7162.1 Biological Background: Lateral Inhibitionand Hebbian Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7162.2 From Von Marsburg and Willshaws Self-OrganizationModel to Kohonens SOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7212.3 The SOM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7253 Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7263.1 Convergence and Cost Functions . . . . . . . . . . . . . . . . . . . . . . . . 7263.2 Topological Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7294 Extensions and Links with Other Learning Paradigms . . . . . . . . . . . 7314.1 SOM, Multidimensional Scaling and Principal Manifolds . . . 7324.2 SOM and Mixture Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7384.3 SOM and Kernel Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7405 Applications and Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7435.1 Vector Quantization and Image Compression. . . . . . . . . . . . . . 7435.2 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7445.3 Density Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7455.4 Gene Expression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7475.5 Data Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7495.6 Text Mining and Information Management . . . . . . . . . . . . . . . 7506 Summary and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7611 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7612 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7613 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 7624 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762Neural Systems EngineeringSteve Furber and Steve Temple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7631 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7631.1 The Neuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7641.2 Neural Microarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7661.3 Engineering with Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7661.4 Scoping the Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7671.5 The Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7681.6 Chapter Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769Contents XXIX2 Neural Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7692.1 Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7702.2 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7712.3 Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7713 The Neuron as a Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7723.1 Communicating with Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7723.2 Point-Neuron Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7733.3 The Spike Response Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7743.4 The Izhikevich Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7743.5 Axons: The Hodgkin-Huxley Model . . . . . . . . . . . . . . . . . . . . . . 7763.6 Dendritic Trees and Compartmental Models . . . . . . . . . . . . . . 7763.7 The Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7774 Engineering Neural Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7774.1 Neural Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7784.2 Population Encoding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7784.3 Spatio-Temporal Spike Neurons . . . . . . . . . . . . . . . . . . . . . . . . . 7804.4 Dening Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7804.5 Implementing Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7814.6 Learning, Adapting, and Tuning . . . . . . . . . . . . . . . . . . . . . . . . . 7814.7 Example Neural Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7824.8 Neuromorphic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7825 Large-Scale Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7835.1 Blue Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7835.2 SPINN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7845.3 SpiNNaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7855.4 Virtual Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7865.5 Diverse Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7896 Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7951 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7952 Key Reference Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7953 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7964 Key International Workshop. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7965 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796Articial Brains: An Evolved Neural Net ModuleApproachHugo de Garis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7971 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7972 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7992.1 Some Recent Articial Brain Projects . . . . . . . . . . . . . . . . . . . . 8002.2 Some Other Recent Articial Brain Projects . . . . . . . . . . . . . . 8033 The Evolution of Neural Network Modules . . . . . . . . . . . . . . . . . . . . . 804XXX Contents3.1 The Evolutionary Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8053.2 Our Evolutionary Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8053.3 The Standard Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 8064 The Celoxica Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8065 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8085.1 IMSI (Inter Module Signaling Interface) . . . . . . . . . . . . . . . . . . 8095.2 How Many Modules? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8116 The Robot and Brain-Robot Interface . . . . . . . . . . . . . . . . . . . . . . . . . 8127 Articial Brain Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8147.1 A Simple Articial Brain Architecture . . . . . . . . . . . . . . . . . . . 8157.2 Incrementing the Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8237.3 Why Not Just Program Everything? . . . . . . . . . . . . . . . . . . . . . 8267.4 Evolving Individual Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8278 The Need for Generic Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8308.1 Limitations of Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 8328.2 Evolvability A Key Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8328.3 Book-Keeping of Modules and Circuits . . . . . . . . . . . . . . . . . . . 8339 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8349.1 The China Brain Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83610 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83710.1 Final Word . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8411 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8411.1 Articial Brain Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . 8411.2 Brain Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8421.3 Cognitive Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8421.4 Evolvable Hardware (EHW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8431.5 Gerald Edelman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8431.6 Ethology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8441.7 Genetic Algorithms (GA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8442 Key Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8453 Articial Brain Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8453.1 Markrams Blue Brain Project . . . . . . . . . . . . . . . . . . . . . . . . . 8453.2 Adaptive Developments CCortex . . . . . . . . . . . . . . . . . . . . . . 8453.3 Edelmans Darwin IV Robot Brain . . . . . . . . . . . . . . . . . . . . . 8454 Key International Conferences/Workshops . . . . . . . . . . . . . . . . . . . . . 8464.1 Congress on Evolutionary Computation CEC (IEEE) . . . . . 8464.2 GECCO Genetic and Evolutionary ComputationConference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8464.3 ICES International Conference on Evolvable Systems . . . . . 8474.4 NASA/DoD Conferences on Evolvable Hardware . . . . . . . . . . 847Contents XXXIPart IX Evolutionary ApproachesEvolving Articial Neural Network EnsemblesMd. Monirul Islam and Xin Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8511 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8512 Evolutionary Ensembles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8522.1 An Evolutionary Design System for ANNs EPNet . . . . . . . . 8532.2 Combination Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8552.3 Experimental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8563 Automatic Modularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8594 Negative Correlation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8604.1 Evolutionary Ensembles with NegativeCorrelation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8624.2 Experimental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8645 Constructive Approaches to Ensemble Learning . . . . . . . . . . . . . . . . . 8655.1 Experimental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8686 Multi-Objective Approaches to Ensemble Learning . . . . . . . . . . . . . . 8706.1 Experimental Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8717 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8771 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8772 Key Survey/Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8773 Organizations, Societies, Special Interest Groups . . . . . . . . . . . . . . . . 8784 Research Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8785 Discussion Groups, Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8786 Key International Conferences and Workshops . . . . . . . . . . . . . . . . . . 8787 (Open Source) Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8798 Data Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879An Order Based Memetic Evolutionary Algorithmfor Set Partitioning ProblemsChristine L. Mumford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8811 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8812 A Brief History of Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 8833 A Generic Genetic Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8834 Order Based GAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8865 A Simple Steady-State GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8916 Set Partitioning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8926.1 The Graph Coloring Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 8936.2 The Bin Packing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8946.3 The Examination Timetabling Problem . . . . . . . . . . . . . . . . . . 8946.4 Other Set Partitioning Problems . . . . . . . . . . . . . . . . . . . . . . . . 895XXXII Contents7 Motivation for the Present Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8968 Culberson and Luos Grouping and Reordering Heuristics . . . . . . . . 8989 Modications to a Standard Order Based GAfor Set Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9029.1 Performance Measures/Fitness Values . . . . . . . . . . . . . . . . . . . . 9049.2 Comparing Orde