Information, Uncertainty and Fusion PDF
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INFORMATION, UNCERTAINTY AND FUSION THE KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE INFORMATION, UNCERTAINTY AND FUSION edited by Bernadette Bouchon-Meunier University ofP aris Ronald R. Yager Iona College and Lotfi A.Zadeh University ofC alifornia, Berkeley ,. ~. SPRINGER-SCIENCE+BUSINESS MEDIA, LLC ISBN 978-1-4613-7373-5 ISBN 978-1-4615-5209-3 (eBook) DOI 10.1007/978-1-4615-5209-3 Library of Congress Cataloging-in-Publication Data A CJ.P. Catalogue record for this book is available from the Library of Congress. Copyright © 2000 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2000 Softcover reprint of the hardcover l5t edition 2000 AII rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photo copying, recording, or otherwise, without the prior written permission of the publisher, Springer-Science+Business Media., LLC. Printed on acid-free paper. Table ofContents Preface ix Information RevisionbyTranslation 3 D.Gabbay,O.Rodrigues,A. Russo AmbiguousImplicationsinKnowledge-BasedSystemsDescribed 33 byEntity-CharacteristicTables S.Guiasu FunctionalDependenciesand theDesignofRelationalDatabases 45 InvolvingImpreciseData P.Bose,L.Li6tard AQueryConstructforParaconsistentDatabases 57 R.Bagai FuzzyClassifactoryObjectRecognitionforCrisisDetection 69 H.Larsen,R. R. Yager AnApproachtoUsingDegreesofBeliefinBDIAgents 81 S.Parsons,P.Giorgini RepresentationofComonotonicallyAdditiveFunctionalbyChoquet 93 Integral Y. Narukawa,T. Murofushi,M. Sugeno OnLowerandUpperApproximationofFuzzyMeasuresby 105 k-OrderAdditiveMeasures M.Grabisch GeneratedFuzzyQuantifiersandTheirOrderings 119 E.E. Kerce,M. Mare~, R. Mesiar OnNeurofuzzyandFuzzyDecisionTreeApproaches 131 C.Olaru,L. Wehenkel AFuzzyExtensiontoaTemporalParsimoniousCoveringTheory 147 S. Sandri,J.Wainer TableofContents vi Uncertainty 159 ProbabilitiesofFutureDecisions 161 D.Nilsson,F.V.Jensen RepresentationsIssuesforRiskScenarios 173 S.Langley ZeroProbabilitiesinStochasticalIndependence 185 G.Coletti,R.Scozzafava ComputingtheProbabilityofFormulasRepresentingEvents 197 in ProductSpaces P.A.Monney, B.Anrig UncertaintyHandlingforDistributedDatabaseIntegration 209 and KnowledgeDiscovery S.1.McClean,B.W.Scotney ARule-BasedLanguageforProbabilisticReasoning 221 S.K.M.Wong DerivingPossibilityDistributionsfromDataSetsUsingConfidence 233 IntervalsofProbabilities L.M.deCampos,J.F. Huete FuzzyRandomVariables-BasedModelingwithGA-PAlgorithms 245 L. Sanchez,I.Couso ASeasonalStreamflowForecastingModel UsingNeurofuzzyNetwork 257 R. Ballini,M. Figueiredo,S.Soares,M. Andrade,F. Gomide AllowingImprecisioninBeliefRepresentationUsingFuzzy-Valued 269 BeliefStructures T.Denoeux ATransformationalApproachtoFuzzyPropositions 283 F.Fernandez,J.Gutierrez Four-ValuedLogicsfor ReasoningwithUncertaintyinPrioritizedData 293 O.Arieli vii TableofContents Fusion 305 AMathematicalModelforFuzzyConnectivesand itsApplication 307 toOperatorsBehaviouralStudy E.Trillas,A. Pradera,S. Cubillo InvariancePropertiesofOWAOperators 319 S.Ovchinnikov AggregationOperatorsasSimilarityRelations 331 G. Beliakov SemanticMeaningofFuzzyControlModelsandHeterogeneous 343 AggregationOperators B.Shukhat MaxativeFuzzyPreferenceStructures 355 B.DeBaets,1.Fodor DirectedPossibilisticGraphsand PossibilisticLogic 365 S. Benferhat,D. Dubois,L. Garcia,H. Prade ALogicofSupporters 381 C.Lafage,J. Lang,R. Sabbadin BuildingArgumentationSystemsonSetConstraintLogic 393 R. Haenni,N. Lehmann GeneticFuzzyc-MeansAlgorithmfor AutomaticGeneration 407 ofFuzzyPartitions S.L6pez,L.Magdalena,J. Velasco FastDempster-ShaferClusteringUsingaNeuralNetworkStructure 419 J.Schubert DistributionofContradictiveBeliefMassesinCombination 431 ofBeliefFunctions M. Daniel RelationshipsAmongOrdinalRelationsonaFiniteSetofEvents 447 A. Capotorti,B.Vantaggi Index 459 Preface As we stand at the precipice ofthe twenty first century the ability to capture and transmit copious amounts ofinformation is clearly a defining feature ofthe human race. In order to increase the value of this vast supply of information we must develop means for effectively processing it. Newly emerging disciplines such as InformationEngineeringandSoftComputingarebeingdeveloped inordertoprovide the tools required. Conferences suchas theInternational ConferenceonInformation ProcessingandManagementofUncertaintyinKnowledge-basedSystems(IPMU)are being held to provide forums in which researchers can discuss the latest developments. The recent IPMU conference held at La Sorbonne in Paris brought together someofthe world's leading experts in uncertainty and information fusion. Inthis volumewehaveincludedaselectionofpapersfrom thisconference. What should beclear from looking at this volume is the numberofdifferent ways that are available for representing uncertain information. This variety in representational frameworks is a manifestation ofthe different types ofuncertainty that appear in the information available to the users. Perhaps, the representation with the longest history is probability theory. This representation is best at addressing the uncertainty associated with the occurrence of different values for similarvariables. This uncertaintyisoftendescribedasrandomness. Roughsetscan beseen as atypeofuncertainty thatcandeal effectively withlackofspecificity, itis a powerful tool for manipulating granular information. Fuzzy sets and the related field ofpossibilitytheory have beendeveloped toaddressanothertypeofuncertainty that being a lack ofclarity. By extending the binary based set theory to allow for graded membership, fuzzy sets provide a tool for the representation ofthe types of concepts used by humans, particularly those used in describing their perceptions. TheDempster-Shaferbelieftheory provides aframeworkparticularly well suitedfor representing informationthatis bothrandom andgranular. Underlying much of the processing of information is the need to fuse information, this is especially so in attempts to implement "intelligent" operations. x Thefundamental processofconvertinginformationintoknowledgeis baseduponthe fusion ofinformation. For example, a salient feature distinguishing between data mining and data base querying is that the former generally requires the fusion of information from multiple dataobjects while the latergenerally deals withonedata object at time. Decision making is also an area which requires considerable useof fusion techniques. Here information from multiplesources, satisfactionsto multiple criteria and multiple possible outcomes must all be fused to enable decisions to be made. Thedevelopmentofefficienttoolsforretrievalofdocuments,aconcernwhose importance has increased in proportion to the rapid development ofthe internet, requirestheuseoffusion techniques. Attempts toconstructintelligentagentsrequire the use of sophisticated operations, many of which are of a fusion type, to help model thecomplexwayshumanbeingsinteractwithinformationandeachother. In this volume we have attempted to bring to the reader some ofthe most recentadvancesin information, uncertainty andfusion. Indoingsowe hope tohelp in the comingexploitationofavailableinformation. Information
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