Multi-Agent Artificial Intelligence Framework for Autonomous Decision Support Systems

Abstract

Artificial Intelligence (AI) has rapidly evolved from isolated intelligent models to collaborative Multi-Agent Artificial Intelligence (MAAI) systems capable of solving complex decision-making problems through distributed intelligence, autonomous reasoning, and coordinated task execution. Traditional Decision Support Systems (DSS) generally rely on centralized architectures and predefined decision rules, limiting their adaptability in dynamic and uncertain environments. Recent advances in Multi-Agent Systems (MAS), Large Language Models (LLMs), reinforcement learning, and autonomous reasoning have enabled the development of intelligent decision support frameworks that distribute responsibilities among multiple specialized agents, allowing collaborative planning, negotiation, knowledge sharing, and adaptive decision-making. This study proposes a Multi-Agent Artificial Intelligence Framework for Autonomous Decision Support Systems (MAAI-ADSS) that integrates intelligent perception, knowledge acquisition, distributed reasoning, collaborative decision-making, conflict resolution, adaptive learning, and continuous feedback into a unified computational architecture. The proposed framework employs specialized autonomous agents that communicate, coordinate, and cooperate to generate reliable, explainable, and context-aware decisions for complex environments. A mathematical framework and algorithmic strategy are developed to evaluate decision accuracy, coordination efficiency, response time, scalability, adaptability, explainability, and overall system performance.