Autonomous Decision Systems Using Multi-Agent Artificial Intelligence

Abstract

Autonomous decision systems have become essential in modern intelligent computing, driven by advances in AI and distributed computing. This paper studies multi-agent AI (MAAI) systems, focusing on their theoretical foundations, design methods, and performance before 2018. Multi-agent systems enable decentralized, scalable, and adaptive decision-making by distributing intelligence among interacting agents capable of perception, reasoning, and action. The paper highlights how agent-based models integrate with decision frameworks, where cooperation, coordination, and competition lead to intelligent behavior. It reviews approaches such as rule-based systems, utility models, and reinforcement learning in multi-agent contexts, while addressing challenges like scalability, communication overhead, conflict resolution, and uncertainty. It also examines key developments in distributed AI, including contract net protocols, distributed constraint satisfaction, and game-theoretic methods, along with applications in robotics, smart grids, traffic, and defense. Finally, it discusses system evaluation metrics like efficiency, convergence, and fault tolerance, offering a consolidated reference and identifying future research directions.