# Distributed Constraint Optimization Problems and Applications: A Survey

arXiv: https://arxiv.org/abs/1602.06347 · HTML: https://mlsanity.com/paper/1602.06347/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2016.

## Built on by (most cited first, 26 of 26)

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- [Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions](https://mlsanity.com/paper/2106.03548/) (2021)
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- [Resilient robot teams: a review integrating decentralised control, change-detection, and learning](https://mlsanity.com/paper/2204.10063/) (2022)
- [Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations](https://mlsanity.com/paper/2601.06188/) (2026)
- [Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI](https://mlsanity.com/paper/2604.17240/) (2026)
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- [Learning to Orchestrate Agents under Uncertainty](https://mlsanity.com/paper/2605.27073/) (2026)
- [Large-scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints](https://mlsanity.com/paper/2106.00379/) (2021)
- [Distributed Interaction Graph Construction for Dynamic DCOPs in Cooperative Multi-agent Systems](https://mlsanity.com/paper/2212.03461/) (2022)
- [Distributed course allocation with asymmetric friendships](https://mlsanity.com/paper/2309.09684/) (2023)
- [Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization](https://mlsanity.com/paper/2504.08737/) (2025)
- [GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization](https://mlsanity.com/paper/2508.06899/) (2025)
- [Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale](https://mlsanity.com/paper/2512.18561/) (2025)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models](https://mlsanity.com/paper/2604.18612/) (2026)
- [Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling](https://mlsanity.com/paper/2607.25835/) (2026)
- [Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination](https://mlsanity.com/paper/2609.31963/) (2026)

## Abstract (arXiv)

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.