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Foundations of Inductive Logic Programming

The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.

Thus, clearly, finding out which formulas <j> are logical consequences of some
set of formulas E is crucial to many areas of artificial intelligence, including
inductive logic programming. Accordingly, we would like to have a procedure, an
algorithm, which could find out whether or not E (= 4> is the case. What is an
algorithm? We will only give an informal explanantion here, referring to [HU79,
CLR90] for the more formal details. Intuitively, an algorithm is a procedure, a
specific sequence ...

Inductive Logic Programming

18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008, Proceedings

This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008. The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.

This can lead to subop- timal results given prediction tasks. On the other hand
better results in prediction problems have been achieved by discriminative
learning of MLNs weights given a certain structure. In this paper we propose an
algorithm for learning the structure of MLNs discriminatively by max- imimizing
the conditional likelihood of the query predicates instead of the joint likelihood of
all predicates. The algorithm chooses the structures by maximizing conditional
likelihood and ...

Inductive Logic Programming

10th International Conference, ILP 2000, London, UK, July 24-27, 2000 Proceedings

Shan-HweiNienhuys-Cheng(UniversityofRotterdam,Netherlands) WilliamCohen(WhizbangsLabs,USA) LucDeRaedt(UniversityofFreiburg,Germany) Sa?soD?zeroski(Jo?zefStefanInstitute,Ljubljana) PeterFlach(UniversityofBristol,UK) AlanFrisch(UniversityofYork,UK) KoichiFurukawa(UniversityofKeio,Japan) RoniKhardon(UniversityofEdinburgh,UK) J¨org-UweKietz(SwissLife,Switzerland) NadaLavra?c(Jo?zefStefanInstitute,Slovenia) JohnLloyd(AustralianNationalUniversity,Australia) StanMatwin(UniversityofOttawa,Canada) RaymondMooney(UniversityofTexas,USA) StephenMuggleton(UniversityofYork,UK) DavidPage(UniversityofWisconsin,USA) BernhardPfahringer(UniversityofWaikato,NewZealand) C´elineRouveirol(Universit´edeParis-Sud,France) ClaudeSammut(UniversityofNewSouthWales,Australia) ´ Mich`eleSebag(EcolePolytechnique,France) AshwinSrinivasan(UniversityofOxford,UK) PrasadTadepalli(OregonStateUniversity,USA) StefanWrobel(UniversityofMagdeburg,Germany) AkihiroYamamoto(UniversityofHokkaido,Japan) Additional Referees ´ ErickAlphonse(Universit´edeParis-Sud,France) LiviuBadea(NationalInstituteforResearchandDevelopmentinInformatics, Romania) DamjanDemsar(Jo?zefStefanInstitute,Slovenia) ElisabethGoncalves(Universit´edeParis-Sud,France) MarkoGrobelnik(Jo?zefStefanInstitute,Slovenia) ClaireKennedy(UniversityofBristol,UK) DanielKudenko(UniversityofYork,UK) JohanneMorin(UniversityofOttawa,Canada) TomonobuOzaki(KeioUniversity,Japan) EdwardRoss(UniversityofBristol,UK) LjupcoTodorovski(Jo?zefStefanInstitute,Slovenia) V´eroniqueVentos(Universit´edeParis-Sud,France) VIII ProgramCommitteeandReferees Sponsors of ILP2000 ILPNet2,TheEuropeanNetworkofExcellenceinInductiveLogicProgramming MLNet,TheEuropeanNetworkofExcellenceinMachineLearning CompulogNet,TheEuropeanNetworkofExcellenceinComputationalLogic Table of Contents IInvitedPaper ILP:JustDoIt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 DavidPage II Contributed Papers ANewAlgorithmforLearningRangeRestrictedHornExpressions. . . . . . . 21 MartaArias,RoniKhardon ARe?nementOperatorforDescriptionLogics. . . . . . . . . . . . . . . . . . . . . . . . . 40 LiviuBadea,Shan-HweiNienhuys-Cheng ExecutingQueryPacksinILP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 HendrikBlockeel,LucDehaspe,BartDemoen,GerdaJanssens, JanRamon,HenkVandecasteele ALogicalDatabaseMiningQueryLanguage . . . . . . . . . . . . . . . . . . . . . . . . . . 78 LucDeRaedt Induction of Recursive Theories in the Normal ILP Setting: Issues and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 FlorianaEsposito,DonatoMalerba,FrancescaA. Lisi ExtendingK-MeansClusteringtoFirst-OrderRepresentations. . . . . . . . . . . 112 MathiasKirsten,StefanWrobel TheoryCompletionUsingInverseEntailment . . . . . . . . . . . . . . . . . . . . . . . . . . 130 StephenH. Muggleton,ChristopherH. Bryant SolvingSelectionProblemsUsingPreferenceRelationBasedonBayesian Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 TomofumiNakano,NobuhiroInuzuka ConcurrentExecutionofOptimalHypothesisSearchforInverse Entailment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 HayatoOhwada,HiroyukiNishiyama,FumioMizoguchi UsingILPtoImprovePlanninginHierarchicalReinforcementLearning. . . 174 MarkReid,MalcolmRyan X TableofContents TowardsLearninginCARIN-ALN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 C´elineRouveirol,V´eroniqueVentos InverseEntailmentinNonmonotonicLogicPrograms. . . . . . . . . . . . . . . . . . . 209 ChiakiSakama ANoteonTwoSimpleTransformationsforImprovingtheE?ciencyofan ILPSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 V´?torSantosCosta,AshwinSrinivasan,RuiCamacho SearchingtheSubsumptionLattic

10th International Conference, ILP 2000, London, UK, July 24-27, 2000
Proceedings James Cussens, Alan Frisch. A New Algorithm for Learning Range
Restricted Horn Expressions⋆ (Extended Abstract) Marta Arias and Roni
Khardon Division of Informatics, University of Edinburgh The King's Buildings,
Edinburgh EH9 3JZ, Scotland {marta ...