# A Game-Theoretic Utility Network for Cooperative Multi-Agent Decisions in Adversarial Environments

arXiv: https://arxiv.org/abs/2004.10950 · HTML: https://mlsanity.com/paper/2004.10950/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

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

- [Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies](https://mlsanity.com/paper/2302.13132/) (2023)
- [A Strategy-Oriented Bayesian Soft Actor-Critic Model](https://mlsanity.com/paper/2303.04193/) (2023)
- [Self-Adaptive Swarm System (SASS)](https://mlsanity.com/paper/2106.04679/) (2021)

## Abstract (arXiv)

Underlying relationships among multi-agent systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. We measure the performance of MAS achieving tasks from the perspective of balancing success probability and system costs. This paper proposes a new network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. This is combined with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain to evaluate GUT against the state-of-the-art QMIX decision-making method. 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 a higher winning rate.