Game Theory Meets Evolutionary Algorithms: Advancing Multi-Agent System Coordination in Robotics

The rapid proliferation of autonomous robotic systems, from UAV swarms and warehouse fleets to humanoid robots, has placed multi-agent coordination at the forefront of modern robotics research. As these systems grow in scale and heterogeneity, classical centralized control approaches falter under the weight of computational complexity, communication constraints, and environmental uncertainty.

Game theory stands as the established mathematical framework for modelling strategic interactions among self-interested agents. In parallel, evolutionary algorithms provide powerful population-based optimization tools capable of navigating high-dimensional, non-convex landscapes without requiring gradient information. Together, these paradigms represent a potent, increasingly integrated combination in robotic systems, though significant challenges remain in operationalizing this mix in complex, real-world environments.

Recent advances across motion planning, task allocation, distributed optimization, and adversarial robustness have demonstrated the value of each paradigm. Yet the synthesis of game-theoretic strategy with evolutionary search remains a largely open frontier, one with transformative potential for next-generation robotic systems.

This Research Topic aims to consolidate and accelerate progress at the intersection of game theory, evolutionary computation, and multi-agent systems with particular application in robotics. Original, high-quality contributions that advance both the theoretical foundations and practical deployment of coordination mechanisms for multi-robot and multi-agent systems are welcome. A central ambition is to bridge communities that have, until now, largely developed in parallel: control theorists, evolutionary computation researchers, and roboticists deploying coordination architectures in real-world scenarios. By bringing these communities together, this Research Topic aspires to seed a new generation of hybrid methods that are simultaneously principled, scalable, and deployable.

Contributions are welcomed across a broad but focused range of topics, including but not limited to:
- Evolutionary and bio-inspired optimization: genetic algorithms, particle swarm optimization, and differential evolution for equilibrium computation and task allocation
- Game-theoretic coordination frameworks: Nash, Stackelberg, potential, and mean-field games applied to multi-robot systems
- Distributed and decentralized control: scalable coordination under communication constraints, partial observability, and network topology
- Adversarial and resilient multi-agent systems: defense against malicious agents, attack-aware planning, and robust equilibrium strategies
- Learning-augmented game-theoretic methods: reinforcement learning, multi-agent deep learning, and Bayesian approaches integrated with game-theoretic objectives
- Real-world applications: UAV swarms, autonomous ground vehicles, human-robot collaboration, search and rescue, and logistics automation

All article types as detailed in https://www.frontiersin.org/journals/fuels/for-authors/article-types are welcome. Both theoretical contributions with formal guarantees and applied studies with experimental validation are encouraged. Survey and review articles addressing the state of the art at this intersection will also be considered.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Multi-Agent Systems, Game-Theoretic Optimization, Evolutionary Algorithms, Distributed Robotics, Swarm Intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.