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Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 538 (73 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.
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
Logic Programming and Knowledge Representation  the AProlog perspective
 Artificial Intelligence
, 2002
"... In this paper we give a short introduction to logic programming approach to knowledge representation and reasoning. The intention is to help the reader to develop a 'feel' for the field's history and some of its recent developments. The discussion is mainly limited to logic programs u ..."
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Cited by 85 (0 self)
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In this paper we give a short introduction to logic programming approach to knowledge representation and reasoning. The intention is to help the reader to develop a 'feel' for the field's history and some of its recent developments. The discussion is mainly limited to logic programs under the answer set semantics. For understanding of approaches to logic programming build on wellfounded semantics, general theories of argumentation, abductive reasoning, etc., the reader is referred to other publications.
Representing Knowledge in AProlog
"... In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of AProlog, can be used to represent various types of knowledge about the world. By way of example ..."
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Cited by 60 (1 self)
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In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of AProlog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with AProlog can be used to solve various programming tasks.
A Comparison of Reasoning Techniques for Querying Large Description Logic ABoxes
, 2006
"... Abstract. Many modern applications of description logics (DLs) require answering queries over large data quantities, structured according to relatively simple ontologies. For such applications, we conjectured that reusing ideas of deductive databases might improve scalability of DL systems. Hence, i ..."
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Cited by 49 (10 self)
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Abstract. Many modern applications of description logics (DLs) require answering queries over large data quantities, structured according to relatively simple ontologies. For such applications, we conjectured that reusing ideas of deductive databases might improve scalability of DL systems. Hence, in our previous work, we developed an algorithm for reducing a DL knowledge base to a disjunctive datalog program. To test our conjecture, we implemented our algorithm in a new DL reasoner KAON2, which we describe in this paper. Furthermore, we created a comprehensive test suite and used it to conduct a performance evaluation. Our results show that, on knowledge bases with large ABoxes but with simple TBoxes, our technique indeed shows good performance; in contrast, on knowledge bases with large and complex TBoxes, existing techniques still perform better. This allowed us to gain important insights into strengths and weaknesses of both approaches. 1
Simplifying logic programs under uniform and strong equivalence
 In LPNMR’04
, 2004
"... Abstract. We consider the simplification of logic programs under the stablemodel semantics, with respect to the notions of strong and uniform equivalence between logic programs, respectively. Both notions have recently been considered for nonmonotonic logic programs (the latter dates back to the 198 ..."
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Cited by 35 (21 self)
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Abstract. We consider the simplification of logic programs under the stablemodel semantics, with respect to the notions of strong and uniform equivalence between logic programs, respectively. Both notions have recently been considered for nonmonotonic logic programs (the latter dates back to the 1980s, though) and provide semantic foundations for optimizing programs with input. Extending previous work, we investigate syntactic and semantic rules for program transformation, based on proper notions of consequence. We furthermore provide encodings of these notions in answerset programming, and give characterizations of programs which are semantically equivalent to positive and Horn programs, respectively. Finally, we investigate the complexity of program simplification and determining semantical equivalence, showing that the problems range between coNP and Π P 2 complexity, and we present some tractable cases. 1
Definitions in Answer Set Programming
"... In answer set programming, combinatorial search problems are solved by writing logic programs the answer sets of which correspond to solutions. Such programs often contain auxiliary atoms, "defined" in terms of atoms introduced earlier. To prove that the answer sets of a program contai ..."
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Cited by 30 (1 self)
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In answer set programming, combinatorial search problems are solved by writing logic programs the answer sets of which correspond to solutions. Such programs often contain auxiliary atoms, "defined" in terms of atoms introduced earlier. To prove that the answer sets of a program containing definitions correspond to the solutions of the problem we want to solve, we need to understand how adding definitions aects the collection of answer sets. In particular, it is useful to be able to describe the effects of adding definitions to a program with nested expressions, in view of the relation of this class of programs to the input language of the answer set programming system smodels. In this paper we generalize the splitting set theorem to programs with nested expressions and show how this generalization can be used to prove program correctness in answer set programming. We also show that, under certain conditions, adding explicit and recursive definitions to a program with nested expressions extends its answer sets conservatively.
Enhancing the MagicSet Method for Disjunctive Datalog Programs
 In Proc. 20th International Conference on Logic Programming (ICLP 04), Springer LNCS 3132
, 2004
"... Abstract. We present a new technique for the optimization of (partially) bound queries over disjunctive datalog programs. The technique exploits the propagation of query bindings, and extends the MagicSet optimization technique (originally defined for nondisjunctive programs) to the disjunctive ca ..."
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Cited by 26 (6 self)
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Abstract. We present a new technique for the optimization of (partially) bound queries over disjunctive datalog programs. The technique exploits the propagation of query bindings, and extends the MagicSet optimization technique (originally defined for nondisjunctive programs) to the disjunctive case, substantially improving on previously defined approaches. MagicSettransformed disjunctive programs frequently contain redundant rules. We tackle this problem and propose a method for preventing the generation of such superfluous rules during the MagicSet transformation. In addition, we provide an efficient heuristic method for the identification of redundant rules, which can be applied in general, even if MagicSets are not used. We implement all proposed methods in the DLV system – the stateoftheart implementation of disjunctive datalog – and perform some experiments. The experimental results confirm the usefulness of MagicSets for disjunctive datalog, and they highlight the computational gain obtained by our method, which outperforms significantly the previously proposed MagicSet method for disjunctive datalog programs. 1
Answer set programming with clause learning
 In: LPNMR7. LNCS, (2004) 302–313 F. Ricca
, 2004
"... Abstract. A conflict clause represents a backtracking solver’s analysis of why a conflict occurred. This analysis can be used to further prune the search space and to direct the search heuristic. The use of such clauses has been very important in improving the efficiency of satisfiability (SAT) solv ..."
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Cited by 21 (0 self)
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Abstract. A conflict clause represents a backtracking solver’s analysis of why a conflict occurred. This analysis can be used to further prune the search space and to direct the search heuristic. The use of such clauses has been very important in improving the efficiency of satisfiability (SAT) solvers over the past few years, especially on structured problems coming from applications. We describe how we have adapted conflict clause techniques for use in the answer set solver Smodels. We experimentally compare the resulting program to the original Smodels program. We also compare to ASSAT and Cmodels, which take a different approach to adding clauses to constrain an answer set search. 1
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.