Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arise...
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Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally;and for the modeling of business rules and policies, where rules with exceptions are often used. this paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. the system (a) is syntactically compatible with RuleML;(b) features strict and defeasible rules, priorities and two kinds of negation;(c) is based on a translation to logicprogramming with declarative semantics;(d) is flexible and adaptable to different intuitions within defeasible reasoning;and (e) can reason with rules, RDF, RDF Schema and (parts of) OWL ontologies.
We propose a novel formal framework (called 3D-NCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set programming (ASP). 3D-NCDC-ASP extends ...
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We propose a novel formal framework (called 3D-NCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set programming (ASP). 3D-NCDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, andNCDC-ASP to 3D. 3D-NCDC-ASP provides a flexible platform offering different types of reasoning: nonmonotonicreasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D-NCDC-ASP, and illustrate its usefulness with applications.
Probabilistic conditionals are a powerful means for expressing uncertain knowledge. In this paper, we describe a system implemented in Java performing probabilistic reasoning at optimum entropy. It provides nonmonoton...
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ISBN:
(纸本)9783642042379
Probabilistic conditionals are a powerful means for expressing uncertain knowledge. In this paper, we describe a system implemented in Java performing probabilistic reasoning at optimum entropy. It provides nonmonotonic belief change operations like revision and up-date and supports advanced querying facilities including diagnosis and what-if-analysis.
the coupling of description logicreasoning systems with other reasoning formalisms (possibly over the Web) is becoming an important research issue and calls for advanced methods and algorithms. Recently, several noti...
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ISBN:
(纸本)3540252363
the coupling of description logicreasoning systems with other reasoning formalisms (possibly over the Web) is becoming an important research issue and calls for advanced methods and algorithms. Recently, several notions of description logic programs have been introduced, combining rule-based semantics with description logics. Among them are nonmonotonic description logic programs (or dl-programs for short) which combine nonmonotoniclogic programs with description logics under a generalized version of the answer-set and the well-founded semantics, respectively, which are the predominant semantics for nonmonotoniclogic programs. In this paper, we consider some technical issues regarding an efficient implementation for both semantics, which has been realized in a working prototype exploiting the two state-of-art tools DLV and RACER. A major issue in this respect is efficient interfacing between the two reasoning systems at hand, for which we devised special methods. Such methods may fruitfully be used for the implementation of systems of similar nature. Reported experimentation activities with our prototype show that the methods we have developed are effective and are a key for highly optimized nonmonotonic dl-program engines.
Today, there exist many different probabilistic programming languages as well as more inference mechanisms for these languages. Still, most logicprogramming-based languages use backward reasoning based on Selective L...
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Today, there exist many different probabilistic programming languages as well as more inference mechanisms for these languages. Still, most logicprogramming-based languages use backward reasoning based on Selective Linear Definite resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. In order to achieve efficiency, we integrate two logicprogramming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent withthe evidence.
Many NP-complete problems can be encoded in the answer set semantics of logic programs in a very concise way, where the encoding reflects the typical "guess and check" nature of NP problems: the property is ...
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ISBN:
(纸本)354020721X
Many NP-complete problems can be encoded in the answer set semantics of logic programs in a very concise way, where the encoding reflects the typical "guess and check" nature of NP problems: the property is encoded in a way such that polynomial size certificates for it correspond to stable models of a program. However, the problem-solving capacity of full disjunctive logic programs (DLPs) is beyond NP at the second level of the polynomial hierarchy. While problems there also have a "guess and check" structure, an encoding in a DLP is often non-obvious, in particular if the "check" itself is co-NP-complete;usually, such problems are solved by interleaving separate "guess" and "check" programs, where the check is expressed by inconsistency of the check program. We present general transformations of head-cycle free (extended) logic programs into stratified disjunctive logic programs which enable one to integrate such "guess" and "check" programs automatically into a single disjunctive logic program. Our results complement recent results on meta-interpretation in ASP, and extend methods and techniques for a declarative "guess and check" problem solving paradigm through ASP.
We study the fixed-parameter complexity of various problems in Al and nonmonotonicreasoning. We show that a number of relevant parameterized problems in these areas are fixed-parameter tractable. Among these problems...
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ISBN:
(纸本)3540667490
We study the fixed-parameter complexity of various problems in Al and nonmonotonicreasoning. We show that a number of relevant parameterized problems in these areas are fixed-parameter tractable. Among these problems are constraint satisfaction problems with bounded treewidth and fixed domain, restricted satisfiability problems, propositional logicprogramming under the stable model semantics where the parameter is the dimension of a feedback vertex set of the program's dependency graph, and circumscriptive inference from a positive k-CNF restricted to models of bounded size. We also show that circumscriptive inference from a general propositional theory, when the attention is restricted to models of bounded size, is fixed-parameter intractable and is actually complete for a novel fixed-parameter complexity class.
the proceedings contain 16 papers. the special focus in this conference is on logicprogramming. the topics include: Completeness of extended unification based on basic narrowing;implementation of full GHC by communic...
ISBN:
(纸本)9783540515647
the proceedings contain 16 papers. the special focus in this conference is on logicprogramming. the topics include: Completeness of extended unification based on basic narrowing;implementation of full GHC by communicating processes;inference methods and semantics on or-type knowledge bases;access program to minimize redundant refutations on the network database system;eUODHILOS: A general-purpose reasoning assistant system — Concept and implementation —;logic based lexical analyser LAX;extraction of characteristic facts and abstract generation;knowledge representation and reasoning for discourse understanding;proving definite clauses without explicit use of inductions;pseudo extension in default reasoning and belief revision by model inference;an approach to nonmonotonic inference mechanism in production system KORE/IE;nonmonotonic parallel inheritance network;logicprogramming debugger using control flow specification;aLEX: the logicprogramming language with explicit control and without cut-operators;lattice programming methodology;a simple programming system written in GHC and its reflective operations.
Argumentation has gained popularity in AI in recent years to support several activities and forms of reasoning. this talk will trace back the logicprogramming and non-monotonic reasoning origins of two well-known arg...
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ISBN:
(纸本)9783319616605;9783319616599
Argumentation has gained popularity in AI in recent years to support several activities and forms of reasoning. this talk will trace back the logicprogramming and non-monotonic reasoning origins of two well-known argumentation formalisms in AI (namely abstract argumentation and assumption-based argumentation). Finally, the talk will discuss recent developments in AI making use of computational argumentation, in particular to support collaborative decision making.
ID-logic uses ideas from the field of logicprogramming to extend second order logic with non-monotone inductive defintions. In this work, we reformulate the semantics of this logic in terms of approximation theory, a...
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ISBN:
(纸本)3540285385
ID-logic uses ideas from the field of logicprogramming to extend second order logic with non-monotone inductive defintions. In this work, we reformulate the semantics of this logic in terms of approximation theory, an algebraic theory which generalizes the semantics of several non-monotonic reasoning formalisms. this allows us to apply certain abstract modularity theorems, developed within the framework of approximation theory, to ID-logic. As such, we are able to offer elegant and simple proofs of generalizations of known theorems, m well as some new results.
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