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48
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
Extending and Implementing the Stable Model Semantics
, 2002
"... A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities ..."
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Cited by 396 (9 self)
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A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities. A declarative semantics is developed which extends the stable model semantics of normal programs. The computational complexity of the language is shown to be similar to that of normal programs under the stable model semantics. A simple embedding of general weight constraint rules to a small subclass of the language called basic constraint rules is devised. An implementation of the language, the smodels system, is developed based on this embedding. It uses a two level architecture consisting of a frontend and a kernel language implementation. The frontend allows restricted use of variables and functions and compiles general weight constraint rules to basic constraint rules. A major part of the work is the development of an ecient search procedure for computing stable models for this kernel language. The procedure is compared with and empirically tested against satis ability checkers and an implementation of the stable model semantics. It offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation.
Tabled Evaluation with Delaying for General Logic Programs
, 1996
"... SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stackbased memory management, SLDNF is often not appropriate for query evalu ..."
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Cited by 304 (29 self)
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SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stackbased memory management, SLDNF is often not appropriate for query evaluation for three reasons: a) it may not terminate due to infinite positive recursion; b) it may not terminate due to infinite recursion through negation; c) it may repeatedly evaluate the same literal in a rule body, leading to unacceptable performance. We address three problems fir a goaloriented query evaluation of general logic programs by presenting tabled evaluation with delaying (SLG resolution).
Smodels  an Implementation of the Stable Model and WellFounded Semantics for Normal Logic Programs
, 1997
"... The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a range ..."
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Cited by 294 (9 self)
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The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a rangerestricted functionfree normal program. The latter module does not produce the whole set of ground instances of the program but a subset that is sufficient in the sense that no stable models are lost. The implementation of the stable model semantics for ground programs is based on bottomup backtracking search where a powerful pruning method is employed. The pruning method exploits an approximation technique for stable models which is closely related to the wellfounded semantics. One of the advantages of this novel technique is that it can be implemented to work in linear space. This makes it possible to apply the stable model semantics also in areas where resulting programs are highly n...
XSB as an Efficient Deductive Database Engine
 In Proceedings of the ACM SIGMOD International Conference on the Management of Data
, 1994
"... This paper describes the XSB system, and its use as an inmemory deductive database engine. XSB began from a Prolog foundation, and traditional Prolog systems are known to have serious deficiencies when used as database systems. Accordingly, XSB has a fundamental bottomup extension, introduced thro ..."
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Cited by 236 (32 self)
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This paper describes the XSB system, and its use as an inmemory deductive database engine. XSB began from a Prolog foundation, and traditional Prolog systems are known to have serious deficiencies when used as database systems. Accordingly, XSB has a fundamental bottomup extension, introduced through tabling (or memoing) [5], which makes it appropriate as an underlying query engine for deductive database systems. Because it eliminates redundant computation, the tabling extension makes XSB able to compute all modularly stratified datalog programs finitely and with polynomial data complexity. For nonstratified programs, a metainterpreter with the same properties is provided. In addition XSB significantly extends and improves the indexing capabilities over those of standard Prolog. Finally, its syntactic basis in HiLog [2], lends it flexibility for data modelling. The implementation of XSB derives from the WAM, the most common Prolog engine. XSB inherits the WAM's efficiency and can ta...
Efficient Implementation of the Wellfounded and Stable Model Semantics
 Proceedings of the Joint International Conference and Symposium on Logic Programming
, 1996
"... An implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs is presented. It includes two modules: an algorithm for implementing the two semantics for ground programs and an algorithm for computing a grounded version of a rangerestricted funct ..."
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Cited by 142 (15 self)
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An implementation of the wellfounded and stable model semantics for rangerestricted functionfree normal programs is presented. It includes two modules: an algorithm for implementing the two semantics for ground programs and an algorithm for computing a grounded version of a rangerestricted functionfree normal program. The latter algorithm does not produce the whole set of ground instances of the program but a subset which is sufficient in the sense that no stable models are lost. The implementation of the stable model semantics for ground programs is based on bottomup backtracking search. It works in linear space and employs a powerful pruning method based on an approximation technique for stable models which is closely related to the wellfounded semantics. The implementation includes an efficient algorithm for computing the wellfounded model of a ground program. The implementation has been tested extensively and compared with a state of the art implementation of the stable mode...
Encoding Planning Problems in Nonmonotonic Logic Programs
 In Proceedings of the Fourth European Conference on Planning
, 1997
"... . We present a framework for encoding planning problems in logic programs with negation as failure, having computational efficiency as our major consideration. In order to accomplish our goal, we bring together ideas from logic programming and the planning systems graphplan and satplan. We discuss ..."
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Cited by 117 (5 self)
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. We present a framework for encoding planning problems in logic programs with negation as failure, having computational efficiency as our major consideration. In order to accomplish our goal, we bring together ideas from logic programming and the planning systems graphplan and satplan. We discuss different representations of planning problems in logic programs, point out issues related to their performance, and show ways to exploit the structure of the domains in these representations. For our experimentation we use an existing implementation of the stable models semantics called smodels. It turns out that for careful and compact encodings, the performance of the method across a number of different domains, is comparable to that of planners like graphplan and satplan. 1 Introduction Nonmonotonic reasoning was originally motivated by the need to capture in a formal logical system aspects of human commonsense reasoning that enable us to withdraw previous conclusions when new informat...
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 109 (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].