It is well known that modeling with constraints networks require a fair expertise. Thus tools able to automatically generate such networks have gained a major interest. The major contribution of this paper is to set a...
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ISBN:
(纸本)9780769542638
It is well known that modeling with constraints networks require a fair expertise. Thus tools able to automatically generate such networks have gained a major interest. The major contribution of this paper is to set a new framework based on inductive logic programming able to build a constraint model from solutions and non-solutions of related problems. The model is expressed in a middle-level modeling language. On this particular relational learning problem, traditional top-down search methods fall into blind search and bottom-up search methods produce too expensive coverage tests. Recent works in inductive logic programming about phase transition and crossing plateau shows that no general solution can face all these difficulties. In this context, we have designed an algorithm combining the major qualities of these two types of search techniques. We present experimental results on some benchmarks ranging from puzzles to scheduling problems.
In order to find an effective for the disambiguation, we explore the ways of complementing statistical approaches with the use of 'domain theories', and suppose that disambiguation decisions can supply tacit i...
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ISBN:
(纸本)9780878492695
In order to find an effective for the disambiguation, we explore the ways of complementing statistical approaches with the use of 'domain theories', and suppose that disambiguation decisions can supply tacit information about such theories, and the theories can be in part automatically induced from such data. The experiment results can be used successfully in disambiguating other sentences from the same domain.
This paper reviews experiments with an approach to discovery through robot's experimentation in its environment. In addition to discovering laws that enable predictions, we are particularly interested in the mecha...
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In this paper we present a method for semantic annotation of texts, which is based on a deep linguistic analysis (DLA) and inductive logic programming (ILP). The combination of DLA and ILP have following benefits: Man...
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ISBN:
(纸本)9783642177484
In this paper we present a method for semantic annotation of texts, which is based on a deep linguistic analysis (DLA) and inductive logic programming (ILP). The combination of DLA and ILP have following benefits: Manual selection of learning features is not needed. The learning procedure has full available linguistic information at its disposal and it is capable to select relevant parts itself. Learned extraction rules can be easily visualized, understood and adapted by human. A description, implementation and initial evaluation of the method are the main contributions of the paper.
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 cien...
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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.
Background: Chemical compounds affecting a bioactivity can usually be classified into several groups, each of which shares a characteristic substructure. We call these substructures "basic active structures"...
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Background: Chemical compounds affecting a bioactivity can usually be classified into several groups, each of which shares a characteristic substructure. We call these substructures "basic active structures" or BASs. The extraction of BASs is challenging when the database of compounds contains a variety of skeletons. Data mining technology, associated with the work of chemists, has enabled the systematic elaboration of BASs. Results: This paper presents a BAS knowledge base, BASIC, which currently covers 46 activities and is available on the Internet. We use the dopamine agonists D1, D2, and Dauto as examples and illustrate the process of BAS extraction. The resulting BASs were reasonably interpreted after proposing a few template structures. Conclusions: The knowledge base is useful for drug design. Proposed BASs and their supporting structures in the knowledge base will facilitate the development of new template structures for other activities, and will be useful in the design of new lead compounds via reasonable interpretations of active structures.
We propose a 2-stage ILP-based design algorithm for hybrid-HOXCs based optical networks. The hybrid-HOXC consists of an optical waveband cross-connect and an electrical cross-connect which grooms only wavelength paths...
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ISBN:
(纸本)9780819485571;0819485578
We propose a 2-stage ILP-based design algorithm for hybrid-HOXCs based optical networks. The hybrid-HOXC consists of an optical waveband cross-connect and an electrical cross-connect which grooms only wavelength paths. Its effectiveness is evaluated through numerical experiments. Impact of electrical/optical port cost ratio on the total network cost is also investigated.
A new class of message-passing algorithms for motif finding is presented. Motif finding is the problem of identifying a collection of common subsequences within a given set of DNA sequences. It can be cast as an integ...
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ISBN:
(纸本)9781424442959;9781424442966
A new class of message-passing algorithms for motif finding is presented. Motif finding is the problem of identifying a collection of common subsequences within a given set of DNA sequences. It can be cast as an integer linear program (ILP). Message-passing techniques are a computationally efficient alternative to the often infeasible combinatorial solutions to the ILP. We introduce a new graphical representation of the ILP formulation of the problem, and use it to develop new message-passing algorithms for motif finding. Simulation results demonstrate that the new algorithms have better performance and convergence properties than the previously proposed solutions.
Formal Concept Analysis(FCA),inductive logic programming(ILP) and Genetic programming(GP) have received increasing interest since them can be applied to many areas *** their formalisms are so different,these three app...
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Formal Concept Analysis(FCA),inductive logic programming(ILP) and Genetic programming(GP) have received increasing interest since them can be applied to many areas *** their formalisms are so different,these three approaches cannot be integrated easily though they share many common or similar goals and functionalities.A fusion will greatly enhance their problem solving *** this paper,a framework to combine FCA,ILP and GP is *** framework is based on a formalism of logic rules for refinement learning that can include concept and program both induction and evolution using FCA,ILP and *** experiment illustrates that our learner based on the framework is promising by compareing the performance with other learner.
The configurable nature of field-programmable gate arrays (FPGAs) has allowed designers to take advantage of various data flow characteristics in application kernels to create custom architecture implementations, by o...
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The configurable nature of field-programmable gate arrays (FPGAs) has allowed designers to take advantage of various data flow characteristics in application kernels to create custom architecture implementations, by optimising instruction level paralleism (ILP) and pipelining at the register transfer level. However, not all applications are composed of pure data flow kernels. Intermingling of control and data flows in applications offers more interesting challenges in creating custom architectures. The authors present one possible way to take advantage of correlations that may be present among data flow graphs (DFGs) embedded in control flow graphs. In certain cases, where there is sufficient correlation and ILP, the proposed context adaptable architecture (CAA) design methodology results in an interesting and useful custom architecture for such embedded DFGs. Certain other application characteristics may demand the use of alternative methodologies such as partial and dynamic reconfiguration (PDR) and a mixture of PDR and common sub-graph methods (PDR-CSG). The authors present a rigorous analysis, combined with some benchmarking efforts to showcase the differences, advantages and disadvantages of the CAA methodology with other methodologies. The authors also present an analysis of how the core underlying algorithm in our methodology compares with other published algorithms and the differences in resulting designs on an FPGA for a sample set of test cases.
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