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69
The DLV System for Knowledge Representation and Reasoning
 ACM Transactions on Computational Logic
, 2002
"... Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believ ..."
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Cited by 330 (80 self)
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Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believed assumptions, DLP is strictly more expressive than normal (disjunctionfree) logic programming, whose expressiveness is limited to properties decidable in NP. Importantly, apart from enlarging the class of applications which can be encoded in the language, disjunction often allows for representing problems of lower complexity in a simpler and more natural fashion. This paper presents the DLV system, which is widely considered the stateoftheart implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, functionfree disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to ∆P 3complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of
Backjumping for Quantified Boolean Logic Satisfiability
 ARTIFICIAL INTELLIGENCE
, 2001
"... The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas (QBFs) is an important research issue in Artificial Intelligence. Many decision procedures have been proposed in the last few years, most of them based on the Davis, Logemann, Loveland ..."
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Cited by 76 (5 self)
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The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas (QBFs) is an important research issue in Artificial Intelligence. Many decision procedures have been proposed in the last few years, most of them based on the Davis, Logemann, Loveland procedure (DLL) for propositional satisfiability (SAT). In this paper we show how it is possible to extend the conflictdirected backjumping schema for SAT to QBF: when applicable, it allows to jump over existentially quantified literals while backtracking. We introduce solutiondirected backjumping, which allows the same for universally quantified literals. Then, we show how it is possible to incorporate both conflictdirected and solutiondirected backjumping in a DLLbased decision procedure for QBF satisfiability. We also implement and test the procedure: The experimental analysis shows that, because of backjumping, significant speedups can be obtained. While there have been several proposals for backjumping in SAT, this is the first time as far as we know this idea has been proposed, implemented and experimented for QBFs.
Beyond NP: ArcConsistency for Quantified Constraints
, 2002
"... The generalization of the satisfiability problem with arbitrary quantifiers is a challenging problem of both theoretical and practical relevance. Being PSPACEcomplete, it provides a canonical model for solving other PSPACE tasks which naturally arise in AI. ..."
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Cited by 36 (4 self)
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The generalization of the satisfiability problem with arbitrary quantifiers is a challenging problem of both theoretical and practical relevance. Being PSPACEcomplete, it provides a canonical model for solving other PSPACE tasks which naturally arise in AI.
Experimenting with Heuristics for Answer Set Programming
 In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI) 2001
, 2001
"... Answer Set Programming (ASP) is a novel programming paradigm, which allows to solve problems in a simple and highly declarative way. The language of ASP (functionfree disjunctive logic programming) is very expressive, and allows to represent even problems of high complexity (every problem in ..."
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Cited by 34 (21 self)
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Answer Set Programming (ASP) is a novel programming paradigm, which allows to solve problems in a simple and highly declarative way. The language of ASP (functionfree disjunctive logic programming) is very expressive, and allows to represent even problems of high complexity (every problem in the complexity class 2 = NP ).
Default Reasoning from Conditional Knowledge Bases: Complexity and Tractable Cases
 Artif. Intell
, 2000
"... Conditional knowledge bases have been proposed as belief bases that include defeasible rules (also called defaults) of the form " ! ", which informally read as "generally, if then ." Such rules may have exceptions, which can be handled in different ways. A number of entailment ..."
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Cited by 21 (13 self)
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Conditional knowledge bases have been proposed as belief bases that include defeasible rules (also called defaults) of the form " ! ", which informally read as "generally, if then ." Such rules may have exceptions, which can be handled in different ways. A number of entailment semantics for conditional knowledge bases have been proposed in the literature. However, while the semantic properties and interrelationships of these formalisms are quite well understood, about their computational properties only partial results are known so far. In this paper, we fill these gaps and first draw a precise picture of the complexity of default reasoning from conditional knowledge bases: Given a conditional knowledge base KB and a default ! , does KB entail ! ? We classify the complexity of this problem for a number of wellknown approaches (including Goldszmidt et al.'s maximum entropy approach and Geffner's conditional entailment), where we consider the general propositional case as well as natural syntactic restrictions (in particular, to Horn and literalHorn conditional knowledge bases). As we show, the more sophisticated semantics for conditional knowledge bases are plagued with intractability in all these fragments. We thus explore cases in which these semantics are tractable, and find that most of them enjoy this property on feedbackfree Horn conditional knowledge bases, which constitute a new, meaningful class of conditional knowledge bases. Furthermore, we generalize previous tractability results from Horn to qHorn conditional knowledge bases, which allow for a limited use of disjunction. Our results complement and extend previous results, and contribute in refining the tractability/intractability frontier of default reasoning from conditional know...
Answer Set Programming with Templates
 In Proc. NMR 2004
, 2003
"... The work aims at extending Answer Set Programming (ASP) with the possibility of quickly introducing new predefined constructs and to deal with compound data structures. We show how ASP can be extended with `template' predicate's definitions by introducing a wellsuited form of second or ..."
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Cited by 17 (7 self)
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The work aims at extending Answer Set Programming (ASP) with the possibility of quickly introducing new predefined constructs and to deal with compound data structures. We show how ASP can be extended with `template' predicate's definitions by introducing a wellsuited form of second order logics. We present language syntax and give its operational semantics. We show that the theory supporting our ASP extension is sound, and that program encodings are evaluated as e#ciently as ASP programs. Examples show how the extended language increases declarativity, readability, compactness of program encodings and code reusability.
Closing semantic web ontologies
, 2006
"... In this paper, we present a novel formalism of hybrid MKNF knowledge bases, which allows us to seamlessly integrate an arbitrary decidable description logic with logic programming rules. We thus obtain a powerful hybrid formalism that combines the best features of both description logics, such as th ..."
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Cited by 16 (2 self)
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In this paper, we present a novel formalism of hybrid MKNF knowledge bases, which allows us to seamlessly integrate an arbitrary decidable description logic with logic programming rules. We thus obtain a powerful hybrid formalism that combines the best features of both description logics, such as the ability to model taxonomic knowledge, and logic programming, such as the ability to perform nonmonotonic reasoning. Extending DLs with unrestricted rules makes reasoning undecidable. To obtain decidability, we apply the wellknown DLsafety restriction that makes the rules applicable only to explicitly named individuals, and thus trade some expressivity for decidability. We present several reasoning algorithms for different fragments of our logic, as well as the corresponding complexity results. Our results show that, in many cases, the data complexity of reasoning with hybrid MKNF knowledge bases is not higher than the data complexity of reasoning
A ConsistencyBased Approach for Belief Change
, 2003
"... This paper presents a general, consistencybased framework for expressing belief change. ..."
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Cited by 16 (7 self)
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This paper presents a general, consistencybased framework for expressing belief change.
Beyond CNF: A circuitbased QBF solver
 in Proc. of SAT, ser. LNCS
, 2009
"... Abstract. Stateoftheart solvers for Quantified Boolean Formulas (QBF) have employed many techniques from the field of Boolean Satisfiability (SAT) including the use of Conjunctive Normal Form (CNF) in representing the QBF formula. Although CNF has worked well for SAT solvers, recent work has poin ..."
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Cited by 15 (3 self)
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Abstract. Stateoftheart solvers for Quantified Boolean Formulas (QBF) have employed many techniques from the field of Boolean Satisfiability (SAT) including the use of Conjunctive Normal Form (CNF) in representing the QBF formula. Although CNF has worked well for SAT solvers, recent work has pointed out some inherent problems with using CNF in QBF solvers. In this paper, we describe a QBF solver, called CirQit (CirQit) that utilizes a circuit representation rather than CNF. The solver can exploit its circuit representation to avoid many of the problems of CNF. For example, we show how this approach generalizes some previously proposed techniques for overcoming the disadvantages of CNF for QBF solvers. We also show how important techniques like clause and cube learning can be made to work with a circuit representation. Finally, we empirically compare the resulting solver against other stateoftheart QBF solvers, demonstrating that our approach can often outperform these solvers. 1