Symmetry Breaking for Distributed Multi-Context Systems

TL;DR

This paper explores symmetry detection and breaking in heterogeneous multi-context systems to reduce search space complexity, proposing a distributed algorithm and demonstrating computational benefits in answer set programming contexts.

Contribution

It introduces a novel distributed symmetry detection and breaking algorithm tailored for multi-context systems with heterogeneous logics.

Findings

The algorithm correctly identifies symmetries in multi-context systems.

Symmetry breaking improves computational efficiency on benchmark problems.

The approach is applicable to systems using answer set programming.

Abstract

Heterogeneous nonmonotonic multi-context systems (MCS) permit different logics to be used in different contexts, and link them via bridge rules. We investigate the role of symmetry detection and symmetry breaking in such systems to eliminate symmetric parts of the search space and, thereby, simplify the evaluation process. We propose a distributed algorithm that takes a local stance, i.e., computes independently the partial symmetries of a context and, in order to construct potential symmetries of the whole, combines them with those partial symmetries returned by neighbouring contexts. We prove the correctness of our methods. We instantiate such symmetry detection and symmetry breaking in a multi-context system with contexts that use answer set programs, and demonstrate computational benefit on some recently proposed benchmarks.