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38
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 37 (22 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
On Solution Correspondences in Answer Set Programming
 In Proc. of 19th International Joint Conference on Artificial Intelligence, 97–102
, 2005
"... We introduce a general framework for specifying program correspondence under the answerset semantics. The framework allows to define different kinds of equivalence notions, including previously defined notions like strong and uniform equivalence, in which programs are extended with rules from a giv ..."
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Cited by 34 (22 self)
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We introduce a general framework for specifying program correspondence under the answerset semantics. The framework allows to define different kinds of equivalence notions, including previously defined notions like strong and uniform equivalence, in which programs are extended with rules from a given context, and correspondence is determined by means of a binary relation. In particular, refined equivalence notions based on projected answer sets can be defined within this framework, where not all parts of an answer set are of relevance. We study general characterizations of inclusion and equivalence problems, introducing novel semantical structures. Furthermore, we deal with the issue of determining counterexamples for a given correspondence problem, and we analyze the computational complexity of correspondence checking. 1
Semantical Characterizations and Complexity of Equivalences in Answer Set Programming
 ACM TRANSACTIONS ON COMPUTATIONAL LOGIC
, 2007
"... 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 answer sets for any other program R. This property strengthens the equivalence of P and Q with respect to answe ..."
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Cited by 28 (12 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 answer sets for any other program R. This property strengthens the equivalence of P and Q with respect to answer sets (which is the particular case for R =∅), and has its applications in program optimization, verification, and modular logic programming. In this article, we consider more liberal notions of strong equivalence, in which the actual form of R may be syntactically restricted. On the one hand, we consider uniform equivalence where R is a set of facts, rather than a set of rules. This notion, which is wellknown in the area of deductive databases, is particularly useful for assessing whether programs P and Q are equivalent as components of a logic program which is modularly structured. On the other hand, we consider relativized notions of equivalence where R ranges over rules over a fixed alphabet, and thus generalize our results to relativized notions of strong and uniform equivalence. For all these notions, we consider disjunctive logic programs in the propositional (ground) case as well as some restricted classes, providing semantical characterizations and analyzing the computational complexity. Our results, which naturally extend to answer set semantics for programs with strong negation, complement the results on strong
S.: Modularity aspects of disjunctive stable models
 LPNMR 2007. LNCS (LNAI
, 2007
"... Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development. In this paper, we are interested in the area of answerset programming where fully declarative and nonmonotonic languages are applied. In ..."
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Cited by 27 (9 self)
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Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development. In this paper, we are interested in the area of answerset programming where fully declarative and nonmonotonic languages are applied. In this context, obtaining a modular structure for programs is by no means straightforward since the output of an entire program cannot in general be composed from the output of its components. To better understand the effects of disjunctive information on modularity we restrict the scope of analysis to the case of disjunctive logic programs (DLPs) subject to stablemodel semantics. We define the notion of a DLPfunction, where a welldefined input/output interface is provided, and establish a novel module theorem which indicates the compositionality of stablemodel semantics for DLPfunctions. The module theorem extends the wellknown splittingset theorem and enables the decomposition of DLPfunctions given their strongly connected components based on positive dependencies induced by rules. In this setting, it is also possible to split shared disjunctive rules among components using a generalized shifting technique. The concept of modular equivalence is introduced for the mutual comparison of DLPfunctions using a generalization of a translationbased verification method. 1.
Properties of programs with monotone and convex constraints
 In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI05
, 2005
"... We study properties of programs with monotone and convex constraints. We extend to these formalisms concepts and results from normal logic programming. They include tight programs and Fages Lemma, program completion and loop formulas, and the notions of strong and uniform equivalence with their char ..."
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Cited by 19 (4 self)
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We study properties of programs with monotone and convex constraints. We extend to these formalisms concepts and results from normal logic programming. They include tight programs and Fages Lemma, program completion and loop formulas, and the notions of strong and uniform equivalence with their characterizations. Our results form an abstract account of properties of some recent extensions of logic programming with aggregates, especially the formalism of smodels.
Semantic Forgetting in Answer Set Programming
, 2008
"... The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In t ..."
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Cited by 17 (5 self)
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The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.
Strong and uniform equivalence in answerset programming: Characterizations and complexity results for the nonground case
 In Proc. AAAI 2005
, 2005
"... Recent research in nonmonotonic logic programming under the answerset semantics studies different notions of equivalence. In particular, strong and uniform equivalence are proposed as useful tools for optimizing (parts of) a logic program. While previous research mainly addressed propositional (i.e ..."
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Cited by 16 (15 self)
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Recent research in nonmonotonic logic programming under the answerset semantics studies different notions of equivalence. In particular, strong and uniform equivalence are proposed as useful tools for optimizing (parts of) a logic program. While previous research mainly addressed propositional (i.e., ground) programs, we deal here with the more general case of nonground programs, and provide semantical characterizations capturing the essence of equivalence, generalizing the concepts of SEmodels and UEmodels, respectively, as originally introduced for propositional programs. We show that uniform equivalence is undecidable, and we give decidability results and precise complexity bounds for strong equivalence (thereby correcting a previous complexity bound for strong equivalence from the literature) as well as for uniform equivalence for finite vocabularies.
Facts do not Cease to Exist Because They are Ignored: Relativised Uniform Equivalence with AnswerSet Projection
 In Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI 2007
, 2007
"... Recent research in answerset programming (ASP) focuses on different notions of equivalence between programs which are relevant for program optimisation and modular programming. Prominent among these notions is uniform equivalence, which checks whether two programs have the same semantics when joine ..."
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Cited by 14 (11 self)
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Recent research in answerset programming (ASP) focuses on different notions of equivalence between programs which are relevant for program optimisation and modular programming. Prominent among these notions is uniform equivalence, which checks whether two programs have the same semantics when joined with an arbitrary set of facts. In this paper, we study a family of more finegrained versions of uniform equivalence, where the alphabet of the added facts as well as the projection of answer sets is taken into account. The latter feature, in particular, allows the removal of auxiliary atoms in computation, which is important for practical programming aspects. We introduce novel semantic characterisations for the equivalence problems under consideration and analyse the computational complexity for checking these problems. We furthermore provide efficient reductions to quantified propositional logic, yielding a rapidprototyping system for equivalence checking.
SELP  a system for studying strong equivalence between logic programs
 In Proceedings of the 8th International Conference on Logic Programming and Nonmonotonic Reasoning(LPNMR 2005
, 2005
"... Abstract. This paper describes a system called SELP for studying strong equivalence in answer set logic programming. The basic function of the system is to check if two given ground disjunctive logic programs are equivalent, and if not, return a counterexample. This allows us to investigate some in ..."
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Cited by 12 (1 self)
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Abstract. This paper describes a system called SELP for studying strong equivalence in answer set logic programming. The basic function of the system is to check if two given ground disjunctive logic programs are equivalent, and if not, return a counterexample. This allows us to investigate some interesting properties of strong equivalence, such as a complete characterization for a rule to be strongly equivalent to another one, and checking whether a given set of rules is strongly equivalent to another, perhaps simpler set of rules. 1
S.: Towards Implementations for Advanced Equivalence Checking in AnswerSet Programming
 ICLP 2005. LNCS
, 2005
"... Abstract. In recent work, a general framework for specifying program correspondences under the answerset semantics has been defined. The framework allows to define different notions of equivalence, including the wellknown notions of strong and uniform equivalence, as well as refined equivalence no ..."
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Cited by 12 (9 self)
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Abstract. In recent work, a general framework for specifying program correspondences under the answerset semantics has been defined. The framework allows to define different notions of equivalence, including the wellknown notions of strong and uniform equivalence, as well as refined equivalence notions based on the projection of answer sets, where not all parts of an answer set are of relevance (like, e.g., removal of auxiliary letters). In the general case, deciding the correspondence of two programs lies on the fourth level of the polynomial hierarchy and therefore this task can (presumably) not be efficiently reduced to answerset programming. In this paper, we describe an approach to compute program correspondences in this general framework by means of lineartime constructible reductions to quantified propositional logic. We can thus use extant solvers for the latter language as backend inference engines for computing program correspondence problems. We also describe how our translations provide a method to construct counterexamples in case a program correspondence does not hold. 1