GUT: A General Cooperative Multi-Agent Hierarchical Decision Architecture in Adversarial Environments
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Adversarial Robotics is a burgeoning research area in Swarms and Multi-Agent Systems. It mainly focuses on agents working on dangerous, hazardous, and risky environments, which will prevent robots to achieve their tasks smoothly. In Adversarial Environments, the adversaries can be intentional and unintentional based on their needs and motivation. Agents need to adopt suitable strategies according to the current situation maximizing their utility or needs. In this paper, we design a game-like Exploration task, where both intentional (Monsters) and unintentional (Obstacles) adversaries challenge the Explorer robots in achieving their target. In order to mimic the rational decision process of an intelligent agent, we propose a new Game-Theoretic Utility Tree (GUT) architecture combining the core principles of game theory, utility theory, probabilistic graphical models, and tree structure decomposing the high-level strategy to executable lower levels. We show through simulation experiments that through the use of GUT, the Explorer agents can effectively cooperate between themselves and increase the utility of the individual agents and of the global system, and achieve higher success in task completion.
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