I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... sets as the underlying semantics has been considered particularly appealing for abduction, due to its applications in solving constraint satisfaction and other combinatorial problems, in expressing the frame axioms, in reasoning with actions and causality, and in representing the history of a plan [21, 26, 27]. In the context of logic programming, abduction has been investigated from both prooftheoretic and model theoretic perspectives (e.g. 7, 14, 15, 16, 34] One of the most followed definitions of abduction in logic programming is that of Kakas and Mancarella s generalized stable model semantics ....

....results using our Prolog implementation. Although our technical development is based on propositional programs, we will comment on how our rewriting framework can be used for classes of function free programs for proving ground goals. One such class is the so called domain restricted programs [27]. This material is given in Section 4.4. 2 Logic Programming Semantics Here, we consider (normal) logic programs which are sets of rules of the form a b 1 ; b m ; not c 1 ; not c n where m; n 0, and a, b i and c i are atoms of a underlying propositional language L. Here an atom ....

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annual of Mathematics and Artificial Intelligence, 25(3-4):241--273, 1999.

....of programs and the problems they encode. On the issues of representation, it is known that SAT instances can be translated to logic programs locally in linear time. Conversely, the translation from logic programs to SAT instances is much more difficult. First, there is no modular translation [12]. Second, the currently known translations require a substantial amount of extra variables, in the Address correspondence to: Jia Huia You, you cs.ualberta.ca. Department of Computer Science, National Taiwan University, Taipei, Taiwan. Department of Computing Science, University of ....

....programs that can also be translated to SAT instances without using extra variables. The second question is whether it is worthwhile to translate such a program to a 2 literal one using a linear number of extra variables. In this set of experiments, we choose the Blocks World encoding of Niemela [12], whose completion formula is known to characterize the stable models semantics [1] thus there is a translation without using extra variables. It turns out that sato performed well with the right value for the parameter g. A wrong choice could degrade sato s performance dramatically. As for ....

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Math. and Artificial Intelligence, 25(3-4):241--273, 1999.

....some ALP frameworks and other extensions of Logic Programming. ALP has tight connections to Answer Set Programming [32] Recall that the ABDUAL framework [1] is an extension of Answer Set Programming with abduction. Standard ALP (with one negation) is strongly related Stable Logic Programming [69, 75], the restriction of Answer Set Programming [32] to pure logic programs. As mentioned in section 4, an abductive logic framework under the generalized stable semantics can be translated in an equivalent logic program under stable semantics. Consequently, current systems for computing stable models ....

....of Answer Set Programming [32] to pure logic programs. As mentioned in section 4, an abductive logic framework under the generalized stable semantics can be translated in an equivalent logic program under stable semantics. Consequently, current systems for computing stable models such as SMODELS [75] can be used to compute abduction under the generalized stable semantics. Interestingly, there are significant di#erences between in computational models that are developed in both areas. Whereas ALP procedures such as SLDNFA, IFF and ACLP are extensions of SLDNF and operate in a top down way on ....

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

....found its way into mainstream practical logic programming. The recent successes have been sparked by the availability of very ecient inference engines (such as smodels [15] DeRes [3] and DLV [6] and a substantial e ort towards understanding how to write programs under stable models semantics [14, 12]. This has led to the development of a novel programming paradigm, commonly referred to as Answer Set Programming (ASP) ASP is a computation paradigm in which logical theories (Horn clauses with NAF) serve as problem speci cations and solutions are represented by collection of models. ASP has ....

....be in the solution only if no other member of the same department has already been selected. AS produces 2 possible answer sets (for the depts employee predicate) fhhartley; csi; hgerke; mathi; hprasad; eeig fhpfeiffer; csi; hgerke; mathi; hprasad; eeig As recognized by a number of authors [12, 14], the adoption of AS requires a paradigm shift to reconcile the peculiar features of AS with the traditional program view of logic programming. First of all, we need to provide programmers with a way of handling multiple answer sets. One could attempt to restore a more traditional view, where a ....

[Article contains additional citation context not shown here]

I. Niemela. Logic Programs with Stable Model Semantics as a Constraint Programming Paradigm. Annals of Mathematics and AI, 2001.

....found its way into mainstream practical logic programming. The recent successes have been sparked by the availability of very ecient inference engines (such as smodels [19] DeRes [4] and DLV [7] and a substantial e ort towards understanding how to write programs under stable models semantics [18, 15, 14]. This has lead to the development of a novel programming paradigm, commonly referred to as Answer Set Programming (ASP) ASP is a computation paradigm in which logical theories (Horn clauses with negation) serve as problem speci cations and solutions are represented by collection of models. ASP ....

....be in the solution only if no other member of the same department has already been selected. AS produces 2 possible answer sets (for the depts employee predicate) fhhartley; csi; hgerke; mathi; hprasad; eeig fhpfeiffer; csi; hgerke; mathi; hprasad; eeig As recognized by a number of authors [15, 18], the adoption of AS requires a paradigm shift to reconcile the peculiar features of AS with the traditional program view of logic programming. First of all, we need to provide programmers with a way of handling multiple answer sets. One could attempt to restore a more traditional view, where a ....

[Article contains additional citation context not shown here]

I. Niemela. Logic Programs with Stable Model Semantics as a Constraint Programming Paradigm. Annals of Mathematics and AI, 25(3-4):241-273, 1999.

....that if every derived goal is ground, then all the mechanisms given in this paper apply directly. Obviously, if for every rule in the given program a variable that appears in the body also appears in the head, then a ground goal will be rewritten to another ground goal. Domain restricted programs [Niemel a, 1999] can be instantiated only on domain predicates over variables that do not appear in the head so that the resulting non ground programs also satisfy this requirement. For example, the program given in the next section is domain restricted. 6 Applications and experimental results We have ....

I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. Math. and AI, 25(3-4):241--273, 1999.

....the generated programs is evaluated. Keywords: specification languages, logic programming, datalog. 1 Introduction The definition and the implementation of logic based languages allowing for specifying complex problems have recently received much attention in the research community (cf. e.g. [3, 4, 5, 6, 7]) The main features of such languages are: ffl they are highly declarative, and, as such, they are easier to use and simplify the error prone process of writing algorithms; ffl they are executable, so that the defined specification can be actually run and thus constitutes a rapid prototype. ....

....to specify exactly all problems which belong to the complexity class NP in a simple way. This restriction has two advantages: ffl the language is simpler, and easier to learn; ffl a more focused strategy in the search for a solution is possible, thus leading to more efficient programs. Like in [3, 4], we opt for a datalog like, i.e. prolog with no function symbols, syntax. Basically, logical formulae are rules, hence they are more restricted than in general predicate logic. The main difference between np spec and the other languages relies in its semantics, which is based on the notion of ....

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I. Niemela, "Logic programs with stable model semantics as a constraint programming paradigm", Annals of Mathematics and Artificial Intelligence, vol. 25, no. 3,4, pp. 241--273, 1999.

....for graph coloring is the same as the one generated by Spec2SAT with our specification of Section 2. As for related research, we have listed in the introduction several approaches to the solution of problems, ranging from planning to cryptography, based on translation into SAT. Other researchers [10, 22] propose datalog like languages for problem specification. The main difference between np spec and the other languages relies in its semantics, which is based on the notion of model minimality. Alloy Analyzer, a system for reasoning in an extension of first order logic based on a translation to ....

I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. of Mathematics and Artif. Intell., 25(3,4):241--273, 1999.

....in the gray area of program (2) Accordingly, the evaluation of these queries by SLDNF resolution would not terminate. One might think that programs with large gray areas are pretty much useless. But such programs play an important part in the work of the proponents of answer set programming 1 [14, 16] who represent solutions to a problem by answer sets, and not by answer substitutions produced in response to a query, as in conventional logic programming. Instead of Prolog, answer set programming requires a software system capable of computing answer sets. Four such systems were demonstrated at ....

....when logic programs are used instead of classical logic, in view of the nonmonotonic character of negation as failure. 7 The idea of answer set planning is due to Subrahmanian and Zaniolo [20] and the results of computational experiments that use smodels to compute answer sets are reported in [2, 16]. The logic programming representation of the blocks world below follows [3] and is based on some of the ideas of [15, 21, 10, 11] The key element of answer set planning is the representation of a dynamic domain such as the blocks world in the form of a history program a program whose answer ....

Ilkka Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 1999.

.... choice of the history description language AL seems to be more suitable for our purposes that L used in [3] The reasoning algorithms are based on recent discoveries of close relationship between A Prolog and reasoning about e ects of actions [11] and the ideas from answer set programming [10, 13, 9]. This approach of course would be impossible without existence of ecient answer set reasoning systems. Finally, the integration of a diagnostic and other activities is based on the agent architecture from [1] 6 Conclusion The paper describes an ongoing work on the development of a diagnostic ....

Niemela, I. Logic programs with stable model semantics as a constraint programming paradigm. In Annals of Mathematics and Articial Intelligence, 25(3-4), 241{ 273, 1999.

....that if every derived goal is ground, then all the mechanisms given in this paper apply directly. Obviously, if for every rule in the given program a variable that appears in the body also appears in the head, then a ground goal will be rewritten to another ground goal. Domain restricted programs [Niemel a, 1999] can be instantiated only on domain predicates over variables that do not appear in the head so that the resulting non ground programs also satisfy this requirement. For example, the program given in the next section is domain restricted. 6 Applications and experimental results We have ....

I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. Math. and AI, 25(3-4):241--273, 1999.

....and thus this problem can not be efficiently translated to traditional STRIPS planning. The P 2 completeness result implies that even if short secure plans can not be efficiently generated by using systems which allow to solve only problems in NP, such as Blackbox [16] CCALC [21] smodels [25], or satisfiability checkers. The hardness relies on the fact that parallel actions are possible. Note that Baral et al. 1] report the related result that deciding in language A [8] the existence of an, in our terminology, secure sequential plan of polynomially bounded length is P 2 ....

I. Niemela. Logic Programs with Stable Model Semantics as a Constraint Programming Paradigm. AMAI, 25(3-4):241--273, 1999.

.... semantics (Van Gelder et al. 1991) and comparatively recent techniques which can be used for computing answer sets of A Prolog (Cholewi nski et al. 1996; Niemela and Simons, 1997; Faber et al. 1999; Wang and Zaniolo, 2000) The latter form the basis for answer set programming advocated in (Niemela, 1999; Marek and Truszczy nski, 1999) In this section we illustrate how these algorithms can be used to implement the above architecture and to perform the agent s reasoning tasks. Planning In this subsection we discuss model theoretic planning using A Prolog. In this approach, the answer sets of ....

....The current rate of improvement of the systems performance and rapid advances in our understanding of methodology of programming in A Prolog allow us to believe in the practicality of this approach. Some applications using XSB, DLV, Smodels and CCALC can be found in (Watson, 1999b; Soininen and Niemela, 1999; Erdem et al. 2000; McCain and Turner, 98; Cui et al. 1999) and the systems can be reached from http: www.cs.utexas.edu users tag . Acknowledgments We would like to thank V. Lifschitz, E. Erdem, M. Nogueira, J. Minker and the referees for their useful comments. ....

Niemela, I. (1999). Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4):241--271.

....IV show that introducing a stochastic element into a backtrack style SAT or CSP procedure, combined with an appropriate restart strategy, can significantly enhance the procedure s performance. In fact, as we see here, it allows us to solve several previously unsolved problem instances. Niemela [49, 50], in independent work, made randomization and restarts a standard feature of his procedure for finding stable models of propositional theories. Niemela did not study directly the cost profiles of his search procedures, but empirically found that randomization and restarts can greatly boost the ....

Niemela, I.: 1998. Logic programs with stable model semantics as a constraint programming paradigm. Ext. version of paper presented at the Workshop on Computational Aspects of Nonmonotonic Reasoning.

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I. Niemela. Logic Programs with Stable Model Semantics as a Constraint Programming Paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241-- 273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4):241--273, 1999.

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Ilkka Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25:241--273, 1999.

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Ilkka Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Ann. of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. In Proceedings of the Workshop on Computational Aspects of Nonmonotonic Reasoning, pages 72--79, Trento, Italy, May 1998. Helsinki University of Technology, Digital Systems Laboratory, Research Report A52.

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I. Niemela. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3,4):241--273, 1999.

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