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

Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class

Genetic Improvement (GI) is a form of Genetic Programming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combinatorial Interaction Testing (CIT), using automated code transplantation. Our GI-evolved solver achieves overall 17 percent improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem

Doctoral Dissertation Design and Implementation of Linear LogicProgramming Languages

to my wife, Megumi ii

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..

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 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