Formal Verification based on Boolean Expression DiagramsWord文档下载推荐.docx

Formal Verification based on Boolean Expression DiagramsWord文档下载推荐.docx

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3.3.Modelformulation

3.4.Performancetrendsovertime

4.Results

4.1.Supplynetworkperformancetrends

4.2.Frontiermembership

4.3.Facetanalysis

5.Conclusions

5.1.Theintegratedbenchmarkingapproach

5.2.Managerialinsights

AppendixA

References

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243

Whendodifferencesmatter?

On-linefeatureextractionthroughcognitiveeconomy 

OriginalResearchArticle

CognitiveSystemsResearch,Volume6,Issue4,December2005,Pages263-281

DavidJ.Finton

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Foranintelligentagenttobetrulyautonomous,itmustbeabletoadaptitsrepresentationtotherequirementsofitstaskasitinteractswiththeworld.Mostcurrentapproachestoon-linefeatureextractionareadhoc;

incontrast,thispaperderivesprincipledcriteriaforrepresentationaladequacybyapplyingthepsychologicalprincipleofcognitiveeconomytoreinforcementlearning.Thecriteriaareprincipledbecausetheyarebasedonananalysisoftheamountofrewardtheagentforfeitswhenitgeneralizesoverstates.Thisanalysisshowsthataction-valueerrorsaresometimesirrelevant,andthattheagentmayoptimizeitsperformancewithlimitedcognitiveresourcesbygroupingtogetherstateswhosedifferencesdonotmatterinitstask.Thepaperpresentsanalgorithmbasedonthisanalysis,incorporatinganactiveformofQ-learningandpartitioningcontinuousstate-spacesbymergingandsplittingVoronoiregions.Theexperimentsillustrateanewmethodologyfortestingandcomparingrepresentationsbymeansoflearningcurves.Resultsfromthepuck-on-a-hilltaskdemonstratethealgorithm’sabilitytolearneffectiverepresentations,superiortothoseproducedbysomeother,well-known,methods.

1.Introduction

1.1.Effectiverepresentations

1.2.Functionapproximationandvalueprediction

1.3.Featureextractionandstateabstraction

1.4.Adhocapproachestostateabstraction

1.5.Cognitiveeconomy

2.Representationalcriteria

2.1.Preferenceandvaluefunctions

2.2.Representationaladequacy

2.3.Statecompatibility

3.Analgorithmforon-linefeatureextraction

3.1.Thetoplevelofthealgorithm

3.2.Activestateinvestigations

3.3.Stateabstractionmodule

3.4.Methodology

4.Casestudy:

puck-on-a-hilltask

4.1.Analysis

4.2.Results

4.2.1.Generatedrepresentations

4.2.2.Controlrepresentations

4.2.3.Learningcurves

5.Discussionandrelatedwork

5.1.Assumptions

5.2.Nearest-neighborrepresentation

5.3.Activelearning

5.4.Non-activeapproaches

5.5.Statecompatibility

6.Conclusions

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244

Minimizingmakespanonanm-machinere-entrantflowshop 

Computers&

OperationsResearch,Volume35,Issue5,May2008,Pages1684-1696

Seong-WooChoi,Yeong-DaeKim

Thispaperfocusesonanm-machinere-entrantflowshopschedulingproblemwiththeobjectiveofminimizingmakespan.Inthere-entrantflowshopconsideredhere,routesofalljobsareidenticalasinordinaryflowshops,butthejobsmustbeprocessedmultipletimesonthemachines.Wepresentheuristicalgorithms,whicharemodifiedfromwell-knownexistingalgorithmsforthegeneralm-machineflowshopproblemornewlydevelopedinthisresearch.Forevaluationoftheperformanceofthealgorithms,computationalexperimentsareperformedonrandomlygeneratedtestproblemsandresultsarereported.

2.Notationandassumptions

3.Heuristicalgorithms

3.1.Simpleheuristicalgorithms

3.1.1.Lowerbound-basedalgorithm（LBB）

3.1.2.Idletime-basedalgorithm（ITB）

3.1.3.HybridLBB–ITBalgorithm1（HLI1）

3.1.4.HybridLBB–ITBalgorithm（HLI2）

3.2.Constructivealgorithms

3.2.1.ModifiedNEHalgorithm1（MN1）

3.2.2.ModifiedNEHalgorithm2（MN2）

3.2.3.ModifiedNEHalgorithm3（MN3）

3.3.SOalgorithm

3.4.SAalgorithm

4.Computationalexperiments

5.Conclusions

Acknowledgements

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245

Habitatsuitabilitymodellingasamappingtoolformacrobenthiccommunities:

AnexamplefromtheBelgianpartoftheNorthSea 

ContinentalShelfResearch,Volume28,Issue3,15February2008,Pages369-379

S.Degraer,E.Verfaillie,W.Willems,E.Adriaens,M.Vincx,V.VanLancker

Beingecologicallyimportantandwell-known,thespatialdistributionpatternofthemacrobenthosisoftenusedtosupportanecologicallysustainablemarinemanagement.Thoughinmanycasesthemacrobenthicspatialdistributionisrelativelywell-known,thisinformationismerelyrestrictedtopointobservationsatthesamplingstations:

althoughbeingincreasinglydemanded,fullcoveragespatialdistributionmapsaregenerallylacking.Thisstudythereforeaimedatdemonstratingtheusefulnessofhabitatsuitabilitymodellingasafullcoveragemappingtoolwithhighrelevanceformarinemanagementthrough

（1）theconstructionofahabitatsuitabilitymodelforthesoftsedimentmacrobenthiccommunitiesintheBelgianpartoftheNorthSea（BPNS）and

（2）predictingthefullcoveragespatialdistributionofmacrobenthiccommunitieswithintheBPNS.TheBPNSwasselectedasacasestudyareabecauseofthehighdataavailabilityonbothmacrobenthosandenvironmentalcharacteristics.Discriminantfunctionanalysis（DFA）objectivelyselectedmediangrainsizeandsedimentmudcontentandomittedbathymetry,slopeanddistancetothecoasttorepresentthemostimportantenvironmentalvariablesdeterminingthemacrobenthiccommunitydistribution.Theconsequentcrossvalidated,empiricalhabitatsuitabilitymodel,usingbothmediangrainsizeandmudcontent,showedanaposterioriaveragecorrectlyclassifiedinstances（CCI）of79%（community-dependentCCIrangingfrom72%to86%）andaCohen'

skappaof0.71,pointingtowardsaverygoodagreementbetweenmodelpredictionsandobservations.Theapplicationofthehabitatsuitabilitymodelonthefullcoveragemapsofmediangrainsizeandsedimentmudcontent,takenfromliterature,allowedtoreliablyassessthedistributionofthemacrobenthiccommunitieswithin96.3%ofthe53,297BPNSgridcellswitharesolutionof250 

m.NexttoitsapplicabilitytotheBPNS,themodelisfurtheranticipatedtopotentiallyperformwellinthefullSouthernBightoftheNorthSea:

testingisadvisedhere.Sincethehabitatsuitabilityisconsideredfarmorestablethroughtimecomparedtothepermanentlyfluctuatingmacrobenthiccommunities,informationonthehabitatsuitabilityofanareaisconsideredhighlyimportantforascientificallysoundmarinemanagement.

2.Materialsandmethods

2.1.TheBelgianpartoftheNorthSea:

currentknowledge

2.2.Researchstrategy

2.3.Dataavailability

2.3.1.Biologicaldata

2.3.2.Environmentaldata

2.3.2.1.Habitatsuitabilitymodelinputdata

2.3.2.2.Fullcoveragemaps

2.4.Habitatsuitabilitymodelling

2.4.1.Modellingstrategy

2.4.2.Biologicaldataexploration:

communityanalysis

2.4.3.Discriminantfunctionanalysis

2.5.Habitatsuitabilitymapping

3.Results

3.1.Communityanalysis

3.2.Communityhabitatpreferences

3.3.Communityhabitatsuitabilitymodelling

3.3.1.Crossvalidation

3.3.2.Finalmodel

3.4.Habitatsuitabilitymaps

4.Discussion

4.1.Habitatsuitabilitymodel

4.2.Habitatsuitabilitymapping

4.3.Relevanceformarinemanagement

246

Anapplicationofsupportvectormachinesinbankruptcypredictionmodel 

ExpertSystemswithApplications,Volume28,Issue1,January2005,Pages127-135

Kyung-ShikShin,TaikSooLee,Hyun-jungKim

Thisstudyinvestigatestheefficacyofapplyingsupportvectormachines（SVM）tobankruptcypredictionproblem.Althoughitisawell-knownfactthattheback-propagationneuralnetwork（BPN）performswellinpatternrecognitiontasks,themethodhassomelimitationsinthatitisanarttof