Proceedings

Approximate Heuristic Search for Semi-Decentralized Systems

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 12-19.

https://doi.org/10.24963/ijcai.2026/2

Abstract

Achieving optimal coordination in multiagent systems involves a trade-off between intractable centralized planning and suboptimal decentralized execution. We bridge this gap by introducing Approximate Recursive Small Step-Semi-Decentralized A* (RS-SDA*), a tree search algorithm that exploits time-varying centralization during periods of available communication conditioned on the environment or joint actions. By interleaving offline planning with online search and using relaxed heuristics, RS-SDA* achieves high solution quality with reduced computational overhead. SDec-POMDP benchmark experiments show that Approximate RS-SDA* often finds near exact optimal solutions in less than 1% of the time required by exact algorithms. We show scalability in six labyrinth environments both deterministic and stochastic state transitions, and demonstrate real-world feasibility with a multi-drone search-and-rescue simulation in the DARPA Subterranean Challenge cave system.

Mahdi Al-Husseini, Kyle H. Wray, Isaac R. Ward, and Mykel J. Kochenderfer. 2026. Approximate Heuristic Search for Semi-Decentralized Systems. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 12–19. https://doi.org/10.24963/ijcai.2026/2
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