the development of the Grid has opened new possibilities for scientists and engineers to execute large-scale modeling experiments. this has stimulated the generation and development of tools for the creation and manag...
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ISBN:
(纸本)9781424403431
the development of the Grid has opened new possibilities for scientists and engineers to execute large-scale modeling experiments. this has stimulated the generation and development of tools for the creation and management of complex computing experiments in the Grid. Among these, tools for the automation of the programming of experiments play a significant role. In this paper we present GriCoL, which we propose as a simple and efficient language for the description of complex Grid experiments.
Decision procedures are widely used in automatedreasoning tools in order to reason about data structures. In applications, many conjectures fall outside the theory handled by a decision procedure. Often, reasoning ab...
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ISBN:
(纸本)3540482814
Decision procedures are widely used in automatedreasoning tools in order to reason about data structures. In applications, many conjectures fall outside the theory handled by a decision procedure. Often, reasoning about user-defined functions on those data structures is needed. For this, inductive reasoning has to be employed. In this work, classes of function definitions and conjectures are identified for which inductive validity can be automatically decided using implicit induction methods and decision procedures for an underlying theory. the class of equational conjectures considered in this paper significantly extends the results of Kapur & Subramaniam (CADE, 2000) [15], which were obtained using explicit induction schemes. Firstly, nonlinear conjectures can be decided automatically. Secondly, function definitions can use other defined functions in their definitions, thus allowing mutually recursive functions and decidable conjectures about them. thirdly, conjectures can have general terms from the decidable theory on inductive positions. these contributions are crucial for successfully integrating inductive reasoning into decision procedures, thus enabling their use in push-button mode in applications including verification and program analysis.
In this paper, a model for decision-making with linguistic information is discussed based on uncertainty reasoning in the framework of lattice-valued logicthrough an example. In this model, decision-making process is...
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ISBN:
(纸本)9812566902
In this paper, a model for decision-making with linguistic information is discussed based on uncertainty reasoning in the framework of lattice-valued logicthrough an example. In this model, decision-making process is treated as an uncertainty reasoning problem, in which decision-maker's background knowledge about the problem at hand and consultancy experts' assessments on alternatives are regarded as the antecedents of the uncertainty reasoning, the final decision is taken as the conclusion of the uncertainty reasoning, respectively.
We consider the problem of how to use automated techniques to learn simple and compact classification rules from microarray gene expression data. Our approach employs the traditional "genetic programming" (G...
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ISBN:
(纸本)9812566902
We consider the problem of how to use automated techniques to learn simple and compact classification rules from microarray gene expression data. Our approach employs the traditional "genetic programming" (GP) algorithm as a supervised categorization technique, but rather than applying GP to gene expression vectors directly, it applies GP to "enhanced feature vectors" obtained by preprocessing the gene expression data using the Gene Ontology and PIR ontologics. On the two datasets considered, this "GP + enhanced feature vectors" combination succeeds in producing compact and simple classification models with near-optimal classification accuracy. For sake of comparison, we also give results from the combination of support vector machine classification and enhanced feature vectors on the same datasets.
In this paper, we consider an interactive goal programming approach for fuzzy multi objective linear programming application to aggregate production planning problems. Our aim is to determine the overall degree of dec...
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ISBN:
(纸本)9812566902
In this paper, we consider an interactive goal programming approach for fuzzy multi objective linear programming application to aggregate production planning problems. Our aim is to determine the overall degree of decision maker satisfaction withthe multiple fuzzy goal values and to give the exactly satisfactory solution results for decision maker in illustrative example.
We present a program logic, Lc, which modularly reasons about unstructured control flow in machine-language programs. Unlike previous program logics, the basic reasoning units in Lc are multiple-entry and multiple-exi...
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the proceedings contain 131 papers. the special focus in this conference is on Applied Artificial Intelligence. the topics include: Computation with information described in natural language the concept of generalized...
ISBN:
(纸本)9812566902
the proceedings contain 131 papers. the special focus in this conference is on Applied Artificial Intelligence. the topics include: Computation with information described in natural language the concept of generalized-constraint-based computation;learning techniques in service robotic environment;foundations of many-valued reasoning;integrated operations in arctic environments;the role of soft computing in applied sciences;a functional tool for fuzzy first order logic evaluation;field theory and computing with words;new operators for context adaptation of mamdani fuzzy systems;u sing parametric functions to solve systems of linear fuzzy equations - an improved algorithm;numerical implementation strategies of the fuzzy finite element method for application in structural dynamics;environmental/economic dispatch using genetic algorithm and fuzzy number ranking method;minimizing the number of affected concepts in handling inconsistent knowledge;a knowledge management based fuzzy model for intelligent information disposal;a semantical assistant method for grammar parsing;lukasiewicz algebra model of linguistic values of truth and their reasoning;weighting qualitative fuzzy first-order logic and its resolution method;annihilator and alpha-subset;multi-fold fuzzy implicative filter of residuated lattice implication algebras;another approach to test the reliability of a model for calculating fuzzy probabilities;a novel Gaussian processes model for regression and prediction;on PCA error of subject classification;optimized algorithm of discovering functional dependencies with degrees of satisfaction and from analogy reasoning to instances based learning.
the fuzzy approach to regression has been traditionally considered as a problem of linear programming. In this work, we introduce a variety of models founded on quadratic programming together with a set of indices use...
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ISBN:
(纸本)3540454853
the fuzzy approach to regression has been traditionally considered as a problem of linear programming. In this work, we introduce a variety of models founded on quadratic programming together with a set of indices useful to check the quality of the obtained results. In order to test the validness of our proposal, we have done an empirical study and we have applied the models in a case with financial data: the Chilean COPEC Company stock price.
the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. T...
the application of Inductive logicprogramming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. the application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called automatedtheory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present
Traffic congestion problems are now increasingly becoming part of our everyday life. Most people in the developed and developing countries alike are faced with problems of traffic congestion, though the root causes ma...
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ISBN:
(纸本)9780975339367
Traffic congestion problems are now increasingly becoming part of our everyday life. Most people in the developed and developing countries alike are faced with problems of traffic congestion, though the root causes may not necessarily be of same nature. there are many reasons for traffic congestion and it seems that better developed roads, together with transport planning and scheduling are not alone adequate in solving this problem. In this paper we use fuzzy reasoning to ascertain the level of traffic congestion on a road leading to a road junction and provide an estimate of the delay in exiting that junction. this estimated delay can be used in ascertaining whether to proceed on that road or to use an alternate route.
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