The paper describes the framework for management of knowledge represented in the form of default theories in a multi-agent system. The process covers the knowledge acquisition as well as its further sharing and transf...
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ISBN:
(纸本)9781479941438
The paper describes the framework for management of knowledge represented in the form of default theories in a multi-agent system. The process covers the knowledge acquisition as well as its further sharing and transformation. It is shown how two approaches to default rules transformation proposed before can be merged into a coherent system.
To achieve high performance on modern computers, it is vital to map algorithmic parallelism to that inherent in the hardware. From an application developer's perspective, it is also important that code can be main...
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ISBN:
(纸本)9781479961238
To achieve high performance on modern computers, it is vital to map algorithmic parallelism to that inherent in the hardware. From an application developer's perspective, it is also important that code can be maintained in a portable manner across a range of hardware. Here we present targetDP (target Data Parallel), a lightweight programming layer that allows the abstraction of data parallelism for applications that employ structured grids. A single source code may be used to target both thread level parallelism (TLP) and instruction level parallelism (ILP) on either SIMD multi-core CPUs or GPU-accelerated platforms. targetDP is implemented via standard C preprocessor macros and library functions, can be added to existing applications incrementally, and can be combined with higher-level paradigms such as MPI. We present CPU and GPU performance results for a benchmark taken from the lattice Boltzmann application that motivated this work. These demonstrate not only performance portability, but also the optimisation resulting from the intelligent exposure of ILP.
Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency...
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Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs inductive logic programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%
Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the ...
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Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants. Results: We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones. Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.
We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In t...
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We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.
We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In t...
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We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.
Built-in self-test (BIST) is a well-known design technique in which part of a circuit is used to test the circuit itself. BIST plays an important role for embedded memories, which do not have pins or pads exposed towa...
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ISBN:
(纸本)9783981537024
Built-in self-test (BIST) is a well-known design technique in which part of a circuit is used to test the circuit itself. BIST plays an important role for embedded memories, which do not have pins or pads exposed toward the periphery of the chip for testing with automatic test equipment. With the rapidly increasing number of embedded memories in modern SOCs (up to hundreds of memories in each hard macro of the SOC), product designers incur substantial costs of test time (subject to possible power constraints) and BIST logic physical resources (area, routing, power). However, only limited previous work addresses the physical design optimization of BIST logic;notably, Chien et al. [7] optimize BIST design with respect to test time, routing length, and area. In our work, we propose a new three-step heuristic approach to minimize test time as well as test physical layout resources, subject to given upper bounds on power consumption. A key contribution is an integer linear programming ILP framework that determines optimal test time for a given cluster of memories using either one or two BIST controllers, subject to test power limits and with full comprehension of available serialization and parallelization. Our heuristic approach integrates (i) generation of a hypergraph over the memories, with test time-aware weighting of hyperedges, along with top-down, FM-style min-cut partitioning;(ii) solution of an ILP that comprehends parallel and serial testing to optimize test scheduling per BIST controller;and (iii) placement of BIST logic to minimize routing and buffering costs. When evaluated on hard macros from a recent industrial 28nm networking SOC, our heuristic solutions reduce test time estimates by up to 11.57% with strictly fewer BIST controllers per hard macro, compared to the industrial solutions.
Model transformation is defined as a central concept in model driven *** the transformation rules is nontrivial task,where it might be much easier for the experts to provide examples of the transformations rather than...
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Model transformation is defined as a central concept in model driven *** the transformation rules is nontrivial task,where it might be much easier for the experts to provide examples of the transformations rather than specifying complete and consistent *** examples provided by expert represent their knowledge in the ***,it is much beneficial to utilize a set of examples,*** of transformation source and target models,in order to learn transformation *** learning(ML) techniques proved their ability of learning relations and concepts in various *** this paper,we aim to apply inductive logic programming(ILP) for learning the transformation rules between the requirements analysis and software design based on a set of pairs of transformation analysis and design *** and GILPS systems have been employed,individually,to induce the intended transformation rules;however the resultant rules don't accommodate the desire ***,in this paper we focus on identifying the problem of analysis-design transformation and discussing the derived rules as well as the limitations of the current ILP systems.
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.
Fuzzy Description logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as on...
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Fuzzy Description logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of inductive logic programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.
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