Distributed Constraint Optimization Problems and Applications: A Survey

TL;DR

This survey reviews the evolution, extensions, and applications of Distributed Constraint Optimization Problems (DCOPs) in multi-agent systems, highlighting challenges and future directions for efficient resolution algorithms in complex environments.

Contribution

It provides a comprehensive classification of DCOP extensions, analyzes resolution methods, and discusses applications, offering insights and future perspectives in the field.

Findings

Classified multiple DCOP extensions and their applications.

Identified challenges in designing efficient resolution algorithms.

Suggested future research directions for DCOP development.

Abstract

The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.