Sebanyak 7 item atau buku ditemukan

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 ...

Logic Programming

Proceedings of the Tenth International Conference on Logic Programming

The Tenth International Conference on Logic Programming, sponsored by the Association for Logic Programming, is a major forum for presentations of research, applications, and implementations in this important area of computer science. Logic programming is one of the most promising steps toward declarative programming and forms the theoretical basis of the programming language Prolog and it svarious extensions. Logic programming is also fundamental to work in artificial intelligence, where it has been used for nonmonotonic and commonsense reasoning, expert systems implementation, deductive databases, and applications such as computer-aided manufacturing.David S. Warren is Professor of Computer Science at the State University of New York, Stony Brook.Topics covered: Theory and Foundations. Programming Methodologies and Tools. Meta and Higher-order Programming. Parallelism. Concurrency. Deductive Databases. Implementations and Architectures. Applications. Artificial Intelligence. Constraints. Partial Deduction. Bottom-Up Evaluation. Compilation Techniques.

While these proposals have illustrated the importance of such analyses, they lack
formal justification. Moreover, several have been found incorrect. This paper
introduces a novel domain of abstract equation systems describing possible
sharing and definite freeness of terms in a system of equations. A simple and
intuitive abstract unification algorithm is presented, providing the core of a correct
and precise sharing and freeness analysis for logic programs. Our contribution is
not only a ...

Logic Programming '87

Proceedings of the 6th Conference Tokyo, Japan, June 22-24, 1987

This volume contains most of the papers presented at the 6th Logic Programming Conference held in Tokyo, June 22-24, 1987. It is the successor of Lecture Notes in Computer Science volumes 221 and 264. The contents cover foundations, programming, architecture and applications. Topics of particular interest are constraint logic programming and parallelism. The effort to apply logic programming to large-scale realistic problems is another important subject of these proceedings.

CHASSIS, FUJITSU LIMITED 140, Miyamoto, Numazu, Shizuoka 410-03, Japan
ABSTRACT This paper is concerned with an algorithm for identifying an unknown
regular language from examples of its members and non-members. The
algorithm is based on the model inference algorithm given by Shapiro. In our
setting, however, a given first order language for describing a target logic
program has countably many unary predicate symbols: qo, qi, q%, . . .. On the
other hand, the oracle ...

Progress in Artificial Intelligence. Knowledge Extraction, Multi-agent Systems, Logic Programming, and Constraint Solving

10th Portuguese Conference on Artificial Intelligence, EPIA 2001, Porto, Portugal, December 17-20, 2001. Proceedings

This book constitutes the refereed proceedings of the 10th Portuguese Conference on Artificial Intelligence, EPTA 2001, held in Porto, Portugal, in December 2001. The 21 revised long papers and 18 revised short papers were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections on extraction of knowledge from databases, AI techniques for financial time series analysis, multi-agent systems, AI logics and logic programming, constraint satisfaction, and AI planning.

This paper proposes a stochastic, and complete, backtrack search algorithm for
Propositional Satisfiability (SAT). In recent years, randomization has become
pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the
ones based on local search, often re- sort to randomization. Complete algorithms
also resort to randomization. These include, state-of-the-art backtrack search SAT
algorithms that often randomize variable selection heuristics. Moreover, it is plain
that the ...

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 ...

Functional and Constraint Logic Programming

18th International Workshop, WFLP 2009, Brasilia, Brazil, June 28, 2009, Revised Selected Papers

This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Functional and Constraint Logic Programming, WFLP 2009, held in Brasilia, Brazil, in June 2009 as part of RDP 2009, the Federated Conference on Rewriting, Deduction, and Programming. The 9 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 14 initial workshop contributions. The papers cover current research in all areas of functional and constraint logic programming including typical areas of interest, such as foundational issues, language design, implementation, transformation and analysis, software engineering, integration of paradigms, and applications.

This paper presents a taxonomy of some exact, right-to-left, string-matching
algorithms. The taxonomy is based on results obtained by using logic program
transformation over a naive and nondeterministic specification. A derivation of the
search part and some notes about the preprocessing part of each algorithm is
presented. The derivations show several design decisions behind each algorithm
, and allow us to organize the algorithms within a taxonomic tree, giving us a
better ...