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检索条件"任意字段=10th International Conference on Inductive Logic Programming, ILP 2000"

共 59 条记录，以下是21-30 订阅

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Computational logic — CL 2000 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： John Lloyd Veronica Dahl Ulrich Furbach Manfred Kerber Kung-Kiu Lau Catuscia Palamidessi Luís Moniz Pereira Yehoshua Sagiv Peter J. Stuckey

ISBN: (数字)9783540449577

ISBN: (纸本)9783540677970

these are the proceedings of the First international conference on Compu- tional logic (CL 2000) which was held at Imperial College in London from 24th to 28th July, 2000. the theme of the conference covered all aspects of the theory, implementation, and application of computational logic, where computational logic is to be understood broadly as the use of logic in computer science. the conference was collocated with the following events: { 6th international conference on Rules and Objects in Databases (DOOD 2000) { 10th international Workshop on logic-based Program Synthesis and Tra- formation (LOPSTR 2000) { 10th international conference on inductive logic programming (ilp 2000). CL 2000 consisted of seven streams: { Program Development (LOPSTR 2000) { logic programming: theory and Extensions { Constraints { Automated Deduction: Putting theory into Practice { Knowledge Representation and Non-monotonic Reasoning { Database Systems (DOOD 2000) { logic programming: Implementations and Applications. the LOPSTR 2000 workshop constituted the program development stream and the DOOD 2000 conference constituted the database systems stream. Each stream had its own chair and program committee, which autonomously selected the papers in the area of the stream. Overall, 176 papers were submitted, of which 86 were selected to be presented at the conference and appear in these proceedings. the acceptance rate was uniform across the streams. In addition, LOPSTR 2000 accepted about 15 extended abstracts to be presented at the conference in the program development stream.

关键词： Artificial Intelligence Software Engineering/programming and Operating Systems Computer System Implementation Mathematical logic and Formal Languages logics and Meanings of Programs programming Techniques

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inductive logic programming for symbol recognition

Inductive logic programming for symbol recognition

引用

ICDAR2009 - 10th international conference on Document Analysis and Recognition

作者： Santosh, K.C. Lamiroy, Bart Ropers, Jean-Philippe INRIA Nancy - Grand Est - LORIA 615 rue du Jardin Botanique 54600 Villers-lès-Nancy France Nancy-Université - INPL - LORIA Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex France

ISBN: (纸本)9780769537252

In this paper, we make an attempt to use inductive logic programming (ilp) to automatically learn non trivial descriptions of symbols, based on a formal description. this work is a first step in this direction and is rather a proof of concept, rather than a fully operational and robust framework. the overall goal of our approach is to express graphic symbols by a number of primitives that may be of any complexity (i.e. not necessarily just lines or points) and connecting relationships that can be deduced from straightforward state-of-the art image treatment and analysis tools. this representation is then used as an input to an ilp solver, in order to deduce non obvious characteristics that may lead to a more semantic related recognition process. © 2009 IEEE.

关键词： Computer circuits

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Challenges in Relational Learning for Real-Time Systems Applications

引用

18th international conference on inductive logic programming

作者： Bartlett, Mark Bate, Iain Kazakov, Dimitar Univ York Dept Comp Sci Artificial Intelligence Grp York YO10 5DD N Yorkshire England Univ York Dept Comp Sci Real Time Syst Grp York N Yorkshire England

ISBN: (纸本)9783540859277

the problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the program code or by running extensive timing analyses. this paper presents a new approach to the problem based on using Machine Learning in the form of ilp to infer program properties based on sample executions of the code. Additionally, significant improvements in the range of functions learnable and the time taken for learning can be made by the application of more advanced ilp techniques.

关键词： Worst Case Execution Time (WCET) inductive logic programming (ilp) Lazy Learning Symmetry Efficiency

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inductive mercury programming

Inductive mercury programming

引用

16th international conference on inductive logic programming

作者： Fisher, Barnaby Cussens, James Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)9783540738466

We investigate using the Mercury language to implement and design ilp algorithms, presenting our own ilp system IMP. Mercury provides faster execution than Prolog. Since Mercury is a purely declarative language, run-time assertion of induced clauses is prohibited. Instead IMP uses a problem-specific interpreter of ground representations of induced clauses. the interpreter is used both for cover testing and bottom clause generation. the Mercury source for this interpreter is generated automatically from the user's background knowledge using Moose, a Mercury parser generator. Our results include some encouraging results on IMP's cover testing speed, but overall IMP is still generally a little slower than ALEPH.

关键词： Computer programming

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Efficient data structures for inductive logic programming

Efficient data structures for inductive logic programming

引用

13th international conference on inductive logic programming

作者： Fonseca, N Rocha, R Camacho, R Silva, F Univ Porto DCC FC P-4150180 Oporto Portugal Univ Porto LIACC P-4150180 Oporto Portugal Univ Porto Fac Engn P-4200465 Oporto Portugal

ISBN: (纸本)3540201440

this work aims at improving the scalability of memory usage in inductive logic programming systems. In this context, we propose two efficient data structures: the Trie, used to represent lists and clauses;and the RL-Tree, a novel data structure used to represent the clauses coverage. We evaluate their performance in the April system using well known datasets. Initial results show a substantial reduction in memory usage without incurring extra execution time overheads. Our proposal is applicable in any ilp system.

关键词： Data structures

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SUBSUMER: A PROLOG θ-SUBSUMPTION ENGINE

SUBSUMER: A PROLOG θ-SUBSUMPTION ENGINE

引用

26th international conference on logic programming (ICLP)

作者： Santos, Jose Muggleton, Stephen Imperial Coll London Dept Comp London England

ISBN: (纸本)9783939897170

State-of-the-art theta-subsumption engines like Django (C) and Resumer2 (Java) are implemented in imperative languages. Since theta-subsumption is inherently a logic problem, in this paper we explore how to e ffi ciently implement it in Prolog. theta-subsumption is an important problem in computational logic and particularly relevant to the inductive logic programming (ilp) community as it is at the core of the hypotheses coverage test which is often the bottleneck of an ilp system. Also, since most of those systems are implemented in Prolog, they can immediately take advantage of a Prolog based theta-subsumption engine. We present a relatively simple (approximate to 1000 lines in Prolog) but e ffi cient and general theta-subsumption engine, Subsumer. Crucial to Subsumer's performance is the dynamic and recursive decomposition of a clause in sets of independent components. Also important are ideas borrowed from constraint programming that empower Subsumer to e ffi ciently work on clauses with up to several thousand literals and several dozen distinct variables. Using the notoriously challenging Phase Transition dataset we show that, cputime wise, Subsumer clearly outperforms the Django subsumption engine and is competitive with the more sophisticated, state-of-the-art, Resumer2. Furthermore, Subsumer's memory requirements are only a small fraction of those engines and it can handle arbitrary Prolog clauses whereas Django and Resumer2 can only handle Datalog clauses.

关键词： theta-subsumption Prolog inductive logic programming

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the Most Probable Explanation for Probabilistic logic Programs with Annotated Disjunctions 1

引用

24th international conference on inductive logic programming (ilp)

作者： Shterionov, Dimitar Renkens, Joris Vlasselaer, Jonas Kimmig, Angelika Meert, Wannes Janssens, Gerda KULeuven Leuven Belgium

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. this encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

关键词： Probabilistic logic programming Statistical relational learning Most Probable Explanation logic programs with annotated disjunctions ProbLog

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Induction of logic programs based on ψ-terms 10th

引用

10th international conference on Algorithmic Learning theory (ALT 99)

作者： Sasaki, Y NTT Commun Sci Labs Seika Kyoto 6190237 Japan

ISBN: (纸本)3540667482

this paper extends the traditional inductive logic programming (ilp) framework to a psi-term capable ilp framework. Ait-Kaci's psi-terms have interesting and significant properties for markedly widening applicable areas of ilp. For example, psi-terms allow partial descriptions of information, generalization and specialization of sorts (or types) placed instead of function symbols, and abstract descriptions of data using sorts;they have comparable representation power to feature structures used in natural language processing. We have developed an algorithm that learns logic programs based on psi-terms, made possible by a bottom-up approach employing the least general generalization (lgg) extended for psi-terms. As an area of application, we have selected information extraction (IE) tasks in which sort information is crucial in deciding the generality of IE rules. Experiments were conducted on a set of test examples and background knowledge consisting of case frames of newspaper articles. the results showed high precision and recall rates for learned rules for the IE tasks.

关键词： Computer circuits

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Efficient relational learning from sparse data 1

引用

10th international conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2002

作者： Popelínský, Luboš Faculty of Informatics Masaryk University Botanická 68a CZ-602 00 Brno Czech Republic

ISBN: (数字)9783540461487

ISBN: (纸本)3540441271

this work deals with inductive inference of logic programs - relational learning - from examples. the work is, in the first place, application-oriented. It aims at building an easy-to-use relational learner and it focuses on the tasks that are solvable with the tool. Assumption-based learning, the new learning paradigm is introduced and the ABL system WiM is described. A methodology for experimental evaluation of ilp systems is introduced and experiments with WiM are displayed. Two classes of application - database schema redesign and mining in spatial data - that have been successfully solved with WiM are described. © 2002 Springer-Verlag Berlin Heidelberg.

关键词： inductive logic programming (ilp)

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Towards combining inductive logic programming with bayesian networks 1

引用

11th international conference on inductive logic programming, ilp 2001

作者： Kersting, Kristian De Raedt, Luc Institute for Computer Science Machine Learning Lab Albert-Ludwigs-University Georges-Köhler-Allee Gebäude 079 Freiburg i. BrgD-79085 Germany

ISBN: (数字)9783540447979

ISBN: (纸本)9783540425380

Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining inductive logic programming (ilp) with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs. More precisely, we show how to combine the ilp setting learning from interpretations with score-based techniques for learning Bayesian networks. thus, the paper positively answers Koller and Pfeffer’s question, whether techniques from ilp could help to learn the logical component of first order probabilistic models. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Bayesian networks

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