Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring

TL;DR

This paper introduces Distributed Answer Set Coloring (DASC), a distributed ASP solver based on graph coloring, designed to handle large grounded programs by leveraging distributed computing resources.

Contribution

It presents a novel distributed ASP solving approach using graph coloring, addressing ground file size limitations in traditional ASP solvers.

Findings

Initial implementation demonstrates feasibility of distributed ASP solving.

Uses Boost and MPI libraries for efficient parallel computation.

Preliminary results show potential for handling larger problems than traditional solvers.

Abstract

Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for ASP. Yet, the great request of resources for certain types of problems, as the planning ones, still constitutes a big limitation for problem solving. Particularly, in the case the program is grounded before the resolving phase, an exponential blow up of the grounding can generate a huge ground file, infeasible for single machines with limited resources, thus preventing even the discovering of a single non-optimal solution. To address this problem, in this paper we present a distributed approach to ASP solving, exploiting distributed computation benefits in order to overcome the just explained limitations. The here presented tool, which is called Distributed Answer Set Coloring (DASC), is a pure solver based on the well-known Graph Coloring algorithm. DASC is part of a bigger project aiming to bring logic programming into a distributed system, started in 2017 by Federico Igne with mASPreduce and continued in 2018 by Pietro Totis with a distributed grounder. In this paper we present a low level implementation of the Graph Coloring algorithm, via the Boost and MPI libraries for C++. Finally, we provide a few results of the very first working version of our tool, at the moment without any strong optimization or heuristic.