C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs

TL;DR

This paper introduces C-CoCoA, a novel algorithm for solving Functional DCOPs with continuous variables, achieving high-quality solutions efficiently by reducing communication and computation overhead.

Contribution

It adapts the Cooperative Constraint Approximation (CoCoA) algorithm with continuous optimization techniques for Functional DCOPs, improving efficiency and solution quality.

Findings

C-CoCoA reduces communication costs compared to existing algorithms.

It achieves high-quality solutions with lower execution time.

The approach is effective for problems with continuous decision variables.

Abstract

Distributed Constraint Optimization Problems (DCOPs) have been widely used to coordinate interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the variables are continuous decision variables and constraints are in functional form. To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables. The existing F-DCOPs algorithms experience huge computation and communication overhead. This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the existing F-DCOP algorithms.