Abstract

Autonomous decision‑making models are central to enabling intelligent systems to operate effectively in complex and adaptive environments where uncertainty, dynamism, and multi‑agent interactions prevail. Such models empower systems to perceive, reason, plan, and act without explicit human intervention, balancing objectives such as robustness, efficiency, safety, and adaptability. Autonomous decision making draws on cognitive architectures, reinforcement learning, planning under uncertainty, game theory, fuzzy and probabilistic reasoning, and bio‑inspired optimization. These models must handle non‑stationary environments, partial observability, stochastic dynamics, and multi‑objective trade‑offs while ensuring timely, reliable decisions. This paper synthesizes foundational and contemporary approaches for autonomous decision making in complex adaptive systems, including Markov decision processes (MDPs), partially observable MDPs (POMDPs), multi‑agent systems, hierarchical and modular architectures, and hybrid learning‑planning frameworks. We examine methodological considerations for model design, evaluation, and deployment, and discuss advantages and disadvantages of leading approaches. Empirical results from benchmark domains and real‑world applications illustrate performance and adaptability gains. Finally, we propose future research directions, such as human‑AI collaboration, explainability, lifelong learning integration, and ethical considerations, for advancing autonomous decision‑making capabilities in increasingly complex system environments.