---
title: "Context-Aware Multi-Agent Coordination: Learning Correlated Equilibria Under Situational Constraints"
authors:
  - "Libo Zhang"
  - "Zhirui Zeng"
  - "Yang Chen"
  - "Jiamou Liu"
doi: "10.24963/ijcai.2026/53"
url: "https://ijcai.org/proceedings/2026/53"
pdf: "https://ijcai.org/proceedings/2026/0053.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "463-471"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Multi-agent learning"
  - "Agent-based and Multi-agent Systems: Coordination and cooperation"
---

# Context-Aware Multi-Agent Coordination: Learning Correlated Equilibria Under Situational Constraints

Libo Zhang, Zhirui Zeng, Yang Chen, Jiamou Liu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 463-471. DOI: [10.24963/ijcai.2026/53](https://doi.org/10.24963/ijcai.2026/53)

## Abstract

Effective multi-agent coordination requires aligning incentives while adhering to complex requirements. However, real-world systems often impose situational constraints, context-dependent requirements triggered only under specific conditions, which challenge standard Correlated Equilibria (CE) solutions. We propose Situational-Constrained Density-Based Correlated Equilibria (SC-DBCE), a novel concept in Markov Games that formalizes situational constraints as logic implications. To solve this, we introduce Situational-Constrained Correlated Policy Iteration (SC-CPI), a reinforcement learning algorithm employing a smooth Log-Sum-Exp mechanism for constraint optimization. Evaluations on multi-agent games, smart grids, and warehouse robotics demonstrate that SC-CPI consistently outperforms baselines in both equilibrium quality and constraint adherence. To our knowledge, this is the first method learning CE under situational constraints.