# A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents

arXiv: https://arxiv.org/abs/2303.16641 · HTML: https://mlsanity.com/paper/2303.16641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The Robotarium: A remotely accessible swarm robotics research testbed](https://mlsanity.com/paper/1609.04730/) (2016)
- [Computational Results for Extensive-Form Adversarial Team Games](https://mlsanity.com/paper/1711.06930/) (2017)
- [Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System](https://mlsanity.com/paper/2004.10920/) (2020)
- [Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions](https://mlsanity.com/paper/2009.00288/) (2020)
- [How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model](https://mlsanity.com/paper/2105.07443/) (2021)
- [Multi-Robot Adversarial Patrolling: Facing a Full-Knowledge Opponent](https://mlsanity.com/paper/1401.3903/) (2014)
- [Decentralized Game-Theoretic Control for Dynamic Task Allocation Problems for Multi-Agent Systems](https://mlsanity.com/paper/2009.08628/) (2020)

## Built on by (most cited first, 3 of 3)

- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)
- [Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy](https://mlsanity.com/paper/2401.00776/) (2024)
- [Innate-Values-driven Reinforcement Learning based Cognitive Modeling](https://mlsanity.com/paper/2411.09160/) (2024)

## Abstract (arXiv)

Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.