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51
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 456 (102 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
Uniform Equivalence of Logic Programs under the Stable Model Semantics
, 2003
"... In recent research on nonmonotonic logic programming, repeatedly strong equivalence of logic programs P and Q has been considered, which holds if the programs P [ R and Q [ R have the same stable models for any other program R. This property strengthens equivalence of P and Q with respect to sta ..."
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Cited by 54 (14 self)
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In recent research on nonmonotonic logic programming, repeatedly strong equivalence of logic programs P and Q has been considered, which holds if the programs P [ R and Q [ R have the same stable models for any other program R. This property strengthens equivalence of P and Q with respect to stable models (which is the particular case for R = ;), and has an application in program optimization. In this paper, we consider the more liberal notion of uniform equivalence, in which R ranges only over the sets of facts rather than all sets of rules. This notion, which is wellknown, is particularly useful for assessing whether programs P and Q are equivalent as components in a logic program which is modularly structured. We provide semantical characterizations of uniform equivalence for disjunctive logic programs and some restricted classes, and analyze the computational cost of uniform equivalence in the propositional (ground) case. Our results, which naturally extend to answer set semantics, complement the results on strong equivalence of logic programs and pave the way for optimizations in answer set solvers as a tool for inputbased problem solving.
Magic Sets and their Application to Data Integration
 In Proc. International Conference on Database Theory (ICDT 05), Springer LNCS 3363, 2005
, 2005
"... Abstract. We propose a generalization of the wellknown Magic Sets technique to Datalog ¬ programs with (possibly unstratified) negation under stable model semantics. Our technique produces a new program whose evaluation is generally more efficient (due to a smaller instantiation), while preserving ..."
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Cited by 33 (9 self)
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Abstract. We propose a generalization of the wellknown Magic Sets technique to Datalog ¬ programs with (possibly unstratified) negation under stable model semantics. Our technique produces a new program whose evaluation is generally more efficient (due to a smaller instantiation), while preserving soundness under cautious reasoning. Importantly, if the original program is consistent, then full queryequivalence is guaranteed for both brave and cautious reasoning, which turn out to be sound and complete. In order to formally prove the correctness of our Magic Sets transformation, we introduce a novel notion of modularity for Datalog ¬ under the stable model semantics, which is relevant per se. We prove that a module can be evaluated independently from the rest of the program, while preserving soundness under cautious reasoning. For consistent programs, both soundness and completeness are guaranteed for brave reasoning and cautious reasoning as well. Our Magic Sets optimization constitutes an effective method for enhancing the performance of dataintegration systems in which queryanswering is carried out by means of cautious reasoning over Datalog ¬ programs. In fact, preliminary results of experiments in the EU project INFOMIX, show that Magic Sets are fundamental for the scalability of the system. 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 29 (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
Debugging inconsistent answer set programs
 PROCEEDINGS OF THE 11TH INTERNATIONAL WORKSHOP ON NONMONOTONIC REASONING (NMR’06). NUMBER IFI0604 IN TECHNICAL REPORT SERIES, CLAUSTHAL UNIVERSITY OF TECHNOLOGY, INSTITUTE FOR INFORMATICS (2006) 77–83
, 2006
"... In this paper we examine how we can find contradictions from Answer Set Programs (ASP). One of the most important phases of programming is debugging, finding errors that have crept in during program implementation. Current ASP systems are still mostly experimental tools and their support for debuggi ..."
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Cited by 25 (1 self)
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In this paper we examine how we can find contradictions from Answer Set Programs (ASP). One of the most important phases of programming is debugging, finding errors that have crept in during program implementation. Current ASP systems are still mostly experimental tools and their support for debugging is limited. This paper addresses one part of ASP debugging, finding the reason why a program does not have any answer sets at all. The basic idea is to compute diagnoses that are minimal sets of constraints whose removal returns consistency. We compute also conflict sets that are sets of mutually incompatible constraints. The final possible source of inconsistency in an ASP program comes from odd negative loops and we show how these may also be detected. We have created a prototype for the ASP debugger that is itself implemented using ASP.
Computing Preferred Answer Sets by MetaInterpretation in Answer Set Programming
 UNDER CONSIDERATION FOR PUBLICATION IN THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2001
"... Most recently, Answer Set Programming (ASP) has been attracting interest as a new paradigm for problem solving. An important aspect, for which several approaches have been presented, is the handling of preferences between rules. In this paper, we consider the problem of implementing preference handl ..."
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Cited by 25 (7 self)
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Most recently, Answer Set Programming (ASP) has been attracting interest as a new paradigm for problem solving. An important aspect, for which several approaches have been presented, is the handling of preferences between rules. In this paper, we consider the problem of implementing preference handling approaches by means of metainterpreters in Answer Set Programming. In particular, we consider the preferred answer set approaches by Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin. We present suitable metainterpreters for these semantics using DLV, which is an efficient engine for ASP. Moreover, we also present a metainterpreter for the weakly preferred answer set approach by Brewka and Eiter, which uses the weak constraint feature of DLV as a tool for expressing and solving an underlying optimization problem. We also consider advanced metainterpreters, which make use of graphbased characterizations and often allow for more efficient computations. Our approach shows the suitability of ASP in general and of DLV in particular for fast prototyping. This can be fruitfully exploited for experimenting with new languages and knowledgerepresentation formalisms.
Discovering Classes of Strongly Equivalent Logic Programs
 Proceedings of the 19th International Joint Conference on Articial Intelligence (IJCAI05
, 2005
"... In this paper we apply computeraided theorem discovery technique to discover theorems about strongly equivalent logic programs under the answer set semantics. Our discovered theorems capture new classes of strongly equivalent logic programs that can lead to new program simplification rules that p ..."
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Cited by 21 (5 self)
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In this paper we apply computeraided theorem discovery technique to discover theorems about strongly equivalent logic programs under the answer set semantics. Our discovered theorems capture new classes of strongly equivalent logic programs that can lead to new program simplification rules that preserve strong equivalence. Specifically, with the help of computers, we discovered exact conditions that capture the strong equivalence between a rule and the empty set, between two rules, between two rules and one of the two rules, between two rules and another rule, and between three rules and two of the three rules. 1.
On the Effect of Default Negation on the Expressiveness of Disjunctive Rules
, 2001
"... In this paper, the expressive power of disjunctive rules involving default negation is analyzed within a framework based on polynomial, faithful and modular (PFM) translations. The analysis is restricted to the stable semantics of disjunctive logic programs. A particular interest is understanding wh ..."
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Cited by 15 (4 self)
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In this paper, the expressive power of disjunctive rules involving default negation is analyzed within a framework based on polynomial, faithful and modular (PFM) translations. The analysis is restricted to the stable semantics of disjunctive logic programs. A particular interest is understanding what is the effect if default negation is allowed in the heads of disjunctive rules. It is established in the paper that occurrences of default negation can be removed from the heads of rules using a PFM translation when default negation is allowed in the bodies of rules. In this case, we may conclude that default negation appearing in the heads of rules does not affect expressive power of rules. However, in the case that default negation may not be used in the bodies of rules, such a PFM translation is no longer possible. Moreover, there is no PFM translation for removing default negation from the bodies of rules. Consequently, disjunctive logic programs with default negation in the bodies of rules are strictly more expressive than those without.
Knowledge
"... Abstract. The paper focuses on a difficult problem when formalizing knowledge: What about the possible concepts that didn’t make it into the formalization? We call such concepts the unconsidered context of the formalized knowledge and argue that erroneous and inadequate behavior of systems based on ..."
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Cited by 13 (0 self)
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Abstract. The paper focuses on a difficult problem when formalizing knowledge: What about the possible concepts that didn’t make it into the formalization? We call such concepts the unconsidered context of the formalized knowledge and argue that erroneous and inadequate behavior of systems based on formalized knowledge can be attributed to different states of the unconsidered context; either while formalizing or during application of the formalization. We then propose an automatic strategy to identify different states of unconsidered context inside a given formalization and to classify which parts of the formalization to use in a given application situation. The goal of this work is to uncover unconsidered context by observing sucess and failure of a given system in use. The paper closes with the evaluation of the proposed procedures in an error diagnosis scenario featuring a plan based user interface.