Functional and Constraint Logic Programming
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An Improved Reductant Calculus Using Fuzzy Partial Evaluation Techniques
An Improved Reductant Calculus using Fuzzy Partial Evaluation Techniques Pascual Juli´an a Gin´es Moreno b Jaime Penabad c aDept. of Information Technologies bDept. of Computing Systems and Systems cDept. of Mathematics ESI, Univ. of Castilla–La Mancha EPSA, Univ. of Castilla–La Mancha Paseo de la Universidad, 4; Campus Universitario, s/n; 13071 Ciudad Real, Spain 02071 Albacete, Spain Abstract Partial evaluation (PE) is an automatic program transformation technique aiming to obtain, among other advantages, the optimization of a program with respect to parts of its input: hence, it is also known as program specialization. This paper introduces the subject of PE into the field of fuzzy logic programming. We define the concept of PE for multi-adjoint logic programs and goals, and apart from discussing the benefits achieved by this technique, we also introduce in the fuzzy setting a completely novel application of PE which allows us the computation of reductants guaranteeing completeness properties without harming the computational efficiency. Reductants are a special kind of fuzzy rules which constitute an essential theoretical tool for proving correctness properties. As observed in the specialized literature, a multi-adjoint logic program, when interpreted on a partially ordered lattice, has to include all its reductants in order to preserve the (approximate) completeness property. This introduces severe penalties in the implementation of efficient multi- adjoint logic programming systems: not only the size of programs increases but also their execution time. In this paper we define a refinement to the notion of reductant based on PE techniques, that we call P E-reductant. -
Lecture Notes in Computer Science 5321 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan Van Leeuwen
Lecture Notes in Computer Science 5321 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany Nick Bassiliades Guido Governatori Adrian Paschke (Eds.) Rule Representation, Interchange and Reasoning on the Web International Symposium, RuleML 2008 Orlando, FL, USA, October 30-31, 2008 Proceedings 13 Volume Editors Nick Bassiliades Aristotle University of Thessaloniki Department of Informatics Thessaloniki, Greece E-mail: [email protected] Guido Governatori National ICT Australia Queensland Research Laboratory St Lucia, Queensland, Australia E-mail: [email protected] Adrian Paschke Free University Berlin Chair for Corporate Semantic Web Berlin, Germany E-mail: [email protected] Library of Congress Control Number: Applied for CR Subject Classification (1998): D.3.1, F.3.2, H.5.3 LNCS Sublibrary: SL 2 – Programming and Software Engineering ISSN 0302-9743 ISBN-10 3-540-88807-1 Springer Berlin Heidelberg New York ISBN-13 978-3-540-88807-9 Springer Berlin Heidelberg New York This work is subject to copyright. -
Approximate Reasoning by Similarity-Based SLD Resolution
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 275 (2002) 389–426 www.elsevier.com/locate/tcs Approximate reasoning by similarity-based SLD resolution Maria I. Sessa Dip. Matematica e Informatica, Universitaà di Salerno, via S. Allende, I-84081 Baronissi (SA), Italy Received February 2000; revised February 2001; accepted March 2001 Communicated by G. Levi Abstract In (Gerla and Sessa, Fuzzy Logic and Soft Computing, Kluwer, Norwell, 1999, pp. 19–31) a methodology that allows to manage uncertain and imprecise information in the frame of the declarative paradigm of Logic Programming has been proposed. With this aim, a Similarity rela- tion R between function and predicate symbols in the language of a logic program is considered. Approximate inferences are then possible since similarity relation allows us to manage alterna- tive instances of entities that can be considered “equal” with a given degree. The declarative semantics of the proposed transformation technique of logic programs is analyzed. The notion of fuzzy least Herbrand model is also introduced. In this paper the corresponding operational semantics is provided by introducing a modiÿed version of SLD resolution. This top-down refu- tation procedure overcomes failure situations in the uniÿcation process by using the similarity relation. A generalized notion of most general uniÿer provides a numeric value which gives a measure of the exploited approximation. In this way, the SLD resolution is enhanced since it is possible both to handle uncertain or imprecise information, and to compute approximate answer substitutions, with an associated approximation-degree, when failures of the exact inference pro- cess occur.