Clingcon: The Next Generation

We present the third generation of the constraint answer set system clingcon, combining Answer Set Programming (ASP) with finite domain constraint processing (CP). While its predecessors rely on a black-box approach to hybrid solving by integrating the CP solver gecode, the new clingcon system pursues a lazy approach using dedicated constraint propagators to extend propagation in the underlying ASP solver clasp. No extension is needed for parsing and grounding clingcon's hybrid modeling language since both can be accommodated by the new generic theory handling capabilities of the ASP grounder gringo. As a whole, clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies on the grounder gringo and the solver clasp. The new approach of clingcon offers a seamless integration of CP propagation into ASP solving that benefits from the whole spectrum of clasp's reasoning modes, including for instance multi-shot solving and advanced optimization techniques. This is accomplished by a lazy approach that unfolds the representation of constraints and adds it to that of the logic program only when needed. Although the unfolding is usually dictated by the constraint propagators during solving, it can already be partially (or even totally) done during preprocessing. Moreover, clingcon's constraint preprocessing and propagation incorporate several well established CP techniques that greatly improve its performance. We demonstrate this via an extensive empirical evaluation contrasting, first, the various techniques in the context of CSP solving and, second, the new clingcon system with other hybrid ASP systems. Under consideration in Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

Bounded Combinatorial Reconfiguration with Answer Set Programming

We develop an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set Programming (ASP). The general task is to study the solution spaces of source combinatorial problems and to decide whether or not there are sequences of feasible solutions that have special properties. The resulting recongo solver covers all metrics of the solver track in the most recent international competition on combinatorial reconfiguration (CoRe Challenge 2022). recongo ranked first in the shortest metric of the single-engine solvers track. In this paper, we present the design and implementation of bounded combinatorial reconfiguration, and present an ASP encoding of the independent set reconfiguration problem that is one of the most studied combinatorial reconfiguration problems. Finally, we present empirical analysis considering all instances of CoRe Challenge 2022.Comment: 15 page

Doctoral Dissertation Design and Implementation of Linear LogicProgramming Languages

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Compiling Resources in a Linear Logic Programming Language

There have been several proposals for logic programming language based on linear logic: Lolli [6], Lygon [5], LO [3], LinLog [2], Forum [8]. In addition, BinProlog allows the use of linear implications of affine logic (a variant of linear logic) [12, 13]. In these languages, it is possible to create and consume resources dynamically as logical formulas. The efficient handling of resource formulas is therefore an important issue in the implementation of these languages. In [10], N. Tamura and Y. Kaneda proposed an abstract machine called LLPAM which is an extension of the standard WAM [14, 1] for a linear logic programming language called LLP. LLP is a superset of Prolog and a subset of Lolli. However, in the original LLPAM design, a resource formula was stored as a term in a heap memory and was not compiled into LLPAM code. In this paper, we describe an extension of LLPAM for compiling resource formulas. In our extension, resources are compiled into closures which consist of..

Abstract Translating a Linear Logic Programming Language into Java

languages, it is possible to create and consume resources dynamically as logical formulas. The efficient handling of resource formulas is, therefore, an important issue in the implementation of these languages. Lolli, Lygon, and Forum are implemented as interpreter systems; Lolli is on SML and λProlog, Lygon is on Prolog, Forum is on SML, λProlog and Prolog. However, none of them have been implemented in Java. In this paper, we describe the Prolog Café 1 system which translates a linear logic programming language called LLP to Java via the LLPAM [12][5], an extension of the standard WAM [16][1] for LLP. LLP is a superset of Prolog and a subset of Lolli. The main difference from the first implementation [4] is resource compilation. That is to say, resource formulas are compiled into closures which consist of a reference of compiled code and a set of bindings for free variables. Calling these resources is integrated with the ordinary predicate invocation. Prolog Café is portable to any platform supporting Java and easily expandable with increasing Java’s class libraries. In performance, on average, Prolog Caf

Translating a Linear Logic . . .

There have been several proposals for logic programming language based on linear logic: Lolli [8], Lygon [7], LO [3], LinLog [2], Forum [11], HACL [10]. In these languages, it is possible to create and consume resources dynamically as logical formulas. The e#cient handling of resource formulas is, therefore, an important issue in the implementation of these languages. Lolli, Lygon, and Forum are implemented as interpreter systems; Lolli is on SML and #Prolog, Lygon is on Prolog, Forum is on SML, #Prolog and Prolog. However, none of them have been implemented in Java. In thi

Logic Programming in a Fragment of Intuitionistic Temporal Linear Logic

1 Introduction Linear logic was introduced by J.-Y.Girard in 1987 [4] as a resource-consciousrefinement of classical logic. Since then a number of logic programming languages 2 M. Banbara, K. Kang, T. Hirai, and N. Tamura based on linear logic have been proposed: LO[1], ACL[12], Lolli[3][8][9], Lygon[5],Forum[13], and LLP[2][15]

Abstract

There have been several proposals for logic programming language based on linear logic: Lolli [6], Lygon [5], LO [3], LinLog [2], Forum [8]. In addition, BinProlog allows the use of linear implications of affine logic (a variant of linear logic) [12, 13]. In these languages, it is possible to create and consume resources dynamically as logical formulas. The efficient handling of resource formulas is therefore an important issue in the implementation of these languages. In [10], N. Tamura and Y. Kaneda proposed an abstract machine called LLPAM which is an extension of the standard WAM [14, 1] for a linear logic programming language called LLP. LLP is a superset of Prolog and a subset of Lolli. However, in the original LLPAM design, a resource formula was stored as a term in a heap memory and was not compiled into LLPAM code. In this paper, we describe an extension of LLPAM for compiling resource formulas. In our extension, resources are compiled into closures which consist of compiled code and a variable binding environment.