Solving Distributed Constraint Optimization Problems Using Logic Programming

TL;DR

This paper introduces a novel logic programming approach to solve Distributed Constraint Optimization Problems, demonstrating significant speed improvements and broader applicability over traditional methods like DPOP.

Contribution

It formulates DCOPs as logic programs and presents ASP-DPOP, the first DCOP algorithm based on Answer Set Programming, with superior performance and problem-solving capabilities.

Findings

ASP-DPOP is up to 100 times faster than DPOP.

ASP-DPOP can solve problems DPOP cannot due to memory limits.

Demonstrates ASP's broad applicability in multi-agent DCOPs.

Abstract

This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) It shows how one can formulate DCOPs as logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) It demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs. Under consideration in Theory and Practice of Logic Programming (TPLP).