{"type":"ScholarlyArticle","title":"Context-Aware Multi-Agent Coordination: Learning Correlated Equilibria Under Situational Constraints","authors":[{"name":"Libo Zhang","slug":"libo-zhang","given":"Libo","family":"Zhang","affiliation":""},{"name":"Zhirui Zeng","slug":"zhirui-zeng","given":"Zhirui","family":"Zeng","affiliation":""},{"name":"Yang Chen","slug":"yang-chen","given":"Yang","family":"Chen","affiliation":""},{"name":"Jiamou Liu","slug":"jiamou-liu","given":"Jiamou","family":"Liu","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/53","url":"https://ijcai.org/proceedings/2026/53","pdf":"https://ijcai.org/proceedings/2026/0053.pdf","markdown":"https://ijcai.org/proceedings/2026/53.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/53","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":53,"pages":{"first":463,"last":471},"keywords":["Agent-based and Multi-agent Systems: Multi-agent learning","Agent-based and Multi-agent Systems: Coordination and cooperation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}