A Game-Theoretic Utility Network for Cooperative Multi-Agent Decisions in Adversarial Environments
Abstract
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.
Cite
@article{arxiv.2004.10950,
title = {A Game-Theoretic Utility Network for Cooperative Multi-Agent Decisions in Adversarial Environments},
author = {Qin Yang and Ramviyas Parasuraman},
journal= {arXiv preprint arXiv:2004.10950},
year = {2021}
}