Fixed-parameter complexity in AI and nonmonotonic reasoning

Many relevant intractable problems become tractable if some problem parameter is fixed. However, various problems exhibit very different computational properties, depending on how the runtime required for solving them is related to the fixed parameter chosen. The theory of parameterized complexity d...

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Vydáno v:Artificial intelligence Ročník 138; číslo 1; s. 55 - 86
Hlavní autoři: Gottlob, Georg, Scarcello, Francesco, Sideri, Martha
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2002
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ISSN:0004-3702, 1872-7921
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Abstract Many relevant intractable problems become tractable if some problem parameter is fixed. However, various problems exhibit very different computational properties, depending on how the runtime required for solving them is related to the fixed parameter chosen. The theory of parameterized complexity deals with such issues, and provides general techniques for identifying fixed-parameter tractable and fixed-parameter intractable problems. We study the parameterized complexity of various problems in AI and nonmonotonic reasoning. 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 forms of conjunctive database queries, restricted satisfiability problems, propositional logic programming 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.
AbstractList Article included in a special issue devoted to the theme: Knowledge presentation and logic programming. Many relevant intractable problems become tractable if some problem parameter is fixed. However, various problems exhibit very different computational properties, depending on how the runtime required for solving them is related to the fixed parameter chosen. The theory of parameterized complexity deals with such issues, and provides general techniques for identifying fixed-parameter tractable and fixed-parameter intractable problems. Describes a study of the parameterized complexity of various problems in artificial intelligence and non monotonic reasoning. Shows 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 forms of conjunctive database queries, restricted satisfiability problems, propositional logic programming 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. (Original abstract - amended)
Many relevant intractable problems become tractable if some problem parameter is fixed. However, various problems exhibit very different computational properties, depending on how the runtime required for solving them is related to the fixed parameter chosen. The theory of parameterized complexity deals with such issues, and provides general techniques for identifying fixed-parameter tractable and fixed-parameter intractable problems. We study the parameterized complexity of various problems in AI and nonmonotonic reasoning. 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 forms of conjunctive database queries, restricted satisfiability problems, propositional logic programming 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.
Author Gottlob, Georg
Scarcello, Francesco
Sideri, Martha
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  organization: Department of Informatics, Athens University of Economics and Business, GR-10434 Athens, Greece
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Issue 1
Keywords Logic programming
Nonmonotonic reasoning
Constraint satisfaction
Stable models
Prime implicants
Fixed-parameter tractability
Parameterized complexity
Conjunctive queries
Circumscription
Language English
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Snippet Many relevant intractable problems become tractable if some problem parameter is fixed. However, various problems exhibit very different computational...
Article included in a special issue devoted to the theme: Knowledge presentation and logic programming. Many relevant intractable problems become tractable if...
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StartPage 55
SubjectTerms Artificial intelligence
Circumscription
Conjunctive queries
Constraint satisfaction
Fixed-parameter tractability
Knowledge representation
Logic programming
Nonmonotonic reasoning
Parameterized complexity
Prime implicants
Stable models
Title Fixed-parameter complexity in AI and nonmonotonic reasoning
URI https://dx.doi.org/10.1016/S0004-3702(02)00182-0
https://www.proquest.com/docview/57519330
Volume 138
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