We present a new system of resource management for linear logic programming which ensures linearity constraints are satisfied solely by manipulating individual formula tags. In our system, tags are rational numbers, a...
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We present a new system of resource management for linear logic programming which ensures linearity constraints are satisfied solely by manipulating individual formula tags. In our system, tags are rational numbers, and a single bounded interval suffices to characterize all of the available formulas at any point in the proof. This system, which we prove correct directly with respect to the efficient resource management system of Cervesato et al., simplifies and improves upon the tag-frame system of Hodas et al.
A bureaucracy can be viewed as a set of policies that governs the activities of its people. The purpose of these policies is to improve operational effectiveness and efficiency. However, manual administration of these...
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A bureaucracy can be viewed as a set of policies that governs the activities of its people. The purpose of these policies is to improve operational effectiveness and efficiency. However, manual administration of these policies is a tedious and often overwhelming task because it is too cognitively demanding to keep track of the complex relationships between the policies. As a result, these policies often consist of many inconsistencies (conflicts) as they evolve because there is no automated means to aid the administrators in detecting inconsistencies. In this paper, we present an approach that uses abductive logic programming for building a decision support system for the administration of bureaucratic policies. The system will help administrators decide the consistency of a policy with respect to the current set of policies and hence, prevent the introduction of inconsistent policies.
The problem of unifying pairs of terms with respect to an equational theory (as well as detecting the unsatisfiability of a system of equations) is, in general, undecidable. In this work, we define a framework based o...
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The problem of unifying pairs of terms with respect to an equational theory (as well as detecting the unsatisfiability of a system of equations) is, in general, undecidable. In this work, we define a framework based on abstract interpretation for the (static) analysis of the unsatisfiability of equation sets. The main idea behind the method is to abstract the process of semantic unification of equation sets based on narrowing. The method consists of building an abstract narrower for equational theories, and executing the sets of equations to be detected for unsatisfiability in the approximated narrower. As an instance of our framework, we define a new analysis whose accuracy is enhanced by some simple loop-checking technique. This analysis can also be actively used for pruning the search tree of an incremental equational constraint solver, and can be integrated with other methods in the literature. Standard methods are shown to be an instance of our framework. To the best of our knowledge, this is the first framework proposed for approximating equational unification.
We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where th...
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We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.
Aquarius Prolog, a high performance compiler designed and built to test the hypothesis that Prolog can be implemented as efficiently as an imperative language by compiling the more powerful features of logic programmi...
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Aquarius Prolog, a high performance compiler designed and built to test the hypothesis that Prolog can be implemented as efficiently as an imperative language by compiling the more powerful features of logic programming only where they are needed, and then only in the simplest form, is described. The authors begin with some background on logic programming and then discuss the Prolog language in more detail. They present an overview of their compiler, giving its structure and the principles underlying its high performance. They compare their system with two popular high-performance commercial systems and with two implementations of C and conclude with an overview of ways to extend this work
Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program ...
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Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program C4.5 [20]. Broadly speaking, attribute-based learning also includes such approaches to learning as neural networks and nearest neighbor techniques. The advantages of attribute-based learning are: relative simplicity, efficiency, and existence of effective techniques for handling noisy data. However, attribute-based learning is limited to non-relational descriptions of objects in the sense that the learned descriptions do not specify relations among the objects' parts. Attribute-based learning thus has two strong limitations: the background knowledge can be expressed in rather limited form, and the lack of relations makes the concept description language inappropriate for some domains.
In recent years, argumentation has been shown to be an appropriate framework in which logic programming with negation as failure as well as other logics for non-monotonic reasoning can be encompassed. Many of the exis...
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In recent years, argumentation has been shown to be an appropriate framework in which logic programming with negation as failure as well as other logics for non-monotonic reasoning can be encompassed. Many of the existing semantics for negation as failure in logic programming can be understood in a uniform way using argumentation. Moreover, other logics for non-monotonic reasoning that can also be formulated via argumentation can be given new semantics, by a direct extension of the logic programming semantics. In this paper we develop an abstract computational framework where various argumentation semantics can be computed via different parametric variations of a simple basic proof theory. This proof theory is given in terms of derivations of trees where each node in a tree contains an argument (or attack) against its corresponding parent node. The proposed proof theory, defined here for the case of logic programming, generalizes directly to other logics for non-monotonic reasoning that can also be formalized via argumentation. The abstract proof theory forms the basis for developing concrete top-down proof procedures for query evaluation. These proof procedures are obtained by adopting specific search strategies and ways of computing attacks in the particular argumentation framework. For logic programming these procedures can be seen as a generalization of the Eshghi-Kowalski abductive proof procedure that in turn generalizes SLDNF.
We introduce logicWeb, an integration of structured logic programming and the World-Wide Web. We show how logicWeb enables programmable behaviour and state to be incorporated into Web pages, allowing them to be viewed...
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ISBN:
(纸本)9780897917780
We introduce logicWeb, an integration of structured logic programming and the World-Wide Web. We show how logicWeb enables programmable behaviour and state to be incorporated into Web pages, allowing them to be viewed as modules or objects with state. logicWeb renders a Web page as a live information entity, able to determine its own response to user queries, and modify the behaviour of hyperlinks. This amalgamation of logic and the Web makes it possible to reason with Web pages, state relationships between pages, and dynamically generate pages. A prototype system is described, which extends Mosaic with logicWeb capabilities using the Common Client Interface. In addition, we outline a client-based search tool written with logicWeb and compare it with an existing package.
LPNMR is based on very solid theoretical foundations. After nearly twenty years of research, LPNMR languages are expressively rich, and their semantic and computational properties are well understood today. Moreover, ...
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As often observed in the literature, cancer evolution follows a path that is unique to each patient;therefore, classical analysis based on the identification of typical mutations, provides little insight in the unders...
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As often observed in the literature, cancer evolution follows a path that is unique to each patient;therefore, classical analysis based on the identification of typical mutations, provides little insight in the understanding of the general rules that drive cancer genesis and evolution. Recent genome sequencing pipelines allow researchers to retrieve rich genetic and epigenetic information from sampled tissues. Analyzing and comparing the evolution of cancer cells for each patient over a large time span can provide some accurate information and relationships. This paper presents a project for a logic programming based analysis that processes time-related genomic information.
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