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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
Nested expressions in logic programs
 Annals of Mathematics and Artificial Intelligence
, 1999
"... We extend the answer set semantics to a class of logic programs with nested expressions permitted in the bodies and heads of rules. These expressions are formed from literals using negation as failure, conjunction (,) and disjunction (;) that can be nested arbitrarily. Conditional expressions are in ..."
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Cited by 112 (13 self)
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We extend the answer set semantics to a class of logic programs with nested expressions permitted in the bodies and heads of rules. These expressions are formed from literals using negation as failure, conjunction (,) and disjunction (;) that can be nested arbitrarily. Conditional expressions are introduced as abbreviations. The study of equivalent transformations of programs with nested expressions shows that any such program is equivalent to a set of disjunctive rules, possibly with negation as failure in the heads. The generalized answer set semantics is related to the LloydTopor generalization of Clark's completion and to the logic of minimal belief and negation as failure.
A Deductive System for Nonmonotonic Reasoning
 In
, 1997
"... Abstract. Disjunctive Deductive Databases (DDDBs) functionfree disjunctive logic programs with negation in rule bodies allowed have been recently recognized as a powerful tool for knowledge representation and commonsense reasoning. Much research as been spent on issues like semantics and comple ..."
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Cited by 102 (21 self)
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Abstract. Disjunctive Deductive Databases (DDDBs) functionfree disjunctive logic programs with negation in rule bodies allowed have been recently recognized as a powerful tool for knowledge representation and commonsense reasoning. Much research as been spent on issues like semantics and complexity of DDDBs, but the important area of implementing DDDBs has been less addressed so far. However, a thorough investigation thereof is a basic requirement for building systems which render previous foundational work on DDDBs useful for practice. This paper presents the architecture ofa DDDB system currently developed at TU Vienna in the FWF project P11580MAT '~A Query System for Disjunctive Deductive Databases". 1 In t roduct ion The study of integrating databases with logic programming opened in the past the field of deductive databases. Basically, a deductive database is a functionfree logic program, i.e., a datalog program (possibly extended with negation). Several advanced eductive database systems utilize logic programming and extensions thereof or querying relational databases, e.g. [14, 21, 24]. The need for representing disjunctive (or incomplete) information led to Disjunctive Deductive Databases (DDDBs) [18]. They can be seen as functionfree disjunctive logic programs, i.e., disjunctive datalog programs [19, 12]. DDDBs are nowadays widely recognized as a valuable tool for knowledge representation a d reasoning [1, 17, 30, 13, 19]. The strong interest in enhancing deductive databases by disjunction is documented by a number of publications (cf. [17]) and special workshops dedicated to this subject (cf. [30]). An important merit of DDDBs over normal (i.e., disjunctionfree) logic programming is its capability to model incomplete knowledge [1, 17].
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 Actions In Logic Programs And Default Theories
, 1997
"... We address the problem of representing commonsense knowledge about action domains in the formalisms of logic programming and default logic. We employ a methodology proposed by Gelfond and Lifschitz which involves first defining a highlevel language for representing commonsense knowledge about actio ..."
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Cited by 84 (7 self)
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We address the problem of representing commonsense knowledge about action domains in the formalisms of logic programming and default logic. We employ a methodology proposed by Gelfond and Lifschitz which involves first defining a highlevel language for representing commonsense knowledge about actions, and then specifying a translation from the highlevel action language into a generalpurpose formalism, such as logic programming. Accordingly, we define a highlevel action language AC, and specify sound and complete translations of portions of AC into logic programming and default logic. The language AC includes "static causal laws" of the following kind: a fluent formula F can be made true by making a fluent formula G true (or, more precisely, F is caused whenever G is caused). Such propositions are more expressive than the state constraints traditionally used to represent background knowledge. Our translations of AC into logic programming and default logic are simple in part because the noncontrapositive nature of causal laws is easily reflected in such rulebased formalisms.
A Logic Programming Approach to KnowledgeState Planning, II: The DLV System
, 2001
"... In Part I of this series of papers, we have proposed a new logicbased planning language, called K. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of t ..."
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Cited by 80 (28 self)
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In Part I of this series of papers, we have proposed a new logicbased planning language, called K. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. In the present Part II, we describe the DLV planning system, which implements K on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logicbased planning systems such as traditional satisfiability planners. We present a detailed comparison of the system to several stateoftheart conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledgestate problem encoding, the DLV system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.
Formalizing sensing actions  A transition function based approach
, 2001
"... In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent’s knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may nee ..."
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Cited by 75 (25 self)
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In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent’s knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may need to construct more general plans consisting of sensing actions and conditional statements to achieve its goal. In this paper we first develop a highlevel action description language that allows specification of sensing actions and their effects in its domain description and allows queries with conditional plans. We give provably correct translations of domain description in our language to axioms in firstorder logic, and relate our formulation to several earlier formulations in the literature. We then analyze the state space of our formulation and develop several sound approximations that have much smaller state spaces. Finally we define regression of knowledge formulas over conditional plans,
Answer Sets for Propositional Theories
 In Proceedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR
, 2005
"... Abstract. Equilibrium logic, introduced by David Pearce, extends the concept of an answer set from logic programs to arbitrary sets of formulas. Logic programs correspond to the special case in which every formula is a “rule ” — an implication that has no implications in the antecedent (body) and c ..."
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Cited by 70 (8 self)
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Abstract. Equilibrium logic, introduced by David Pearce, extends the concept of an answer set from logic programs to arbitrary sets of formulas. Logic programs correspond to the special case in which every formula is a “rule ” — an implication that has no implications in the antecedent (body) and consequent (head). The semantics of equilibrium logic looks very different from the usual definitions of an answer set in logic programming, as it is based on Kripke models. In this paper we propose a new definition of equilibrium logic which uses the concept of a reduct, as in the standard definition of an answer set. Second, we apply the generalized concept of an answer set to the problem of defining the semantics of aggregates in answer set programming. We propose, in particular, a semantics for weight constraints that covers the problematic case of negative weights. Our semantics of aggregates is an extension of the approach due to Faber, Leone, and Pfeifer to a language with choice rules and, more generally, arbitrary rules with nested expressions. 1
Declarative ProblemSolving Using the DLV System
"... The need for representing indefinite information led to disjunctive deductive databases, which also fertilized work on disjunctive logic programming. Based on this paradigm, the DLV system has been designed and implemented as a tool for declarative knowledge representation. In this paper, we focus o ..."
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Cited by 62 (26 self)
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The need for representing indefinite information led to disjunctive deductive databases, which also fertilized work on disjunctive logic programming. Based on this paradigm, the DLV system has been designed and implemented as a tool for declarative knowledge representation. In this paper, we focus on the usage of DLV for solving problems in a declarative manner and report on experiments that we have run on a suite of benchmark problems. We discuss how problems can be solved in a natural way using a "Guess&Check"paradigm where solutions are guessed and verified by parts of the program. Furthermore, we describe various frontends that can be used for solving problems in specific applications. The experiments show that due to the ongoing implementation efforts, which include finetuning of the underlying algorithms, successive and significant performance improvements have been achieved during the last two years. The results indicate that DLV is capable of solving some complex problems quite efficiently.