Optimizing Decision-Making with AI-Enhanced Support Systems: Leveraging Reinforcement Learning and Bayesian Networks

Abstract

This research paper explores the optimization of decision-making processes through AI-enhanced support systems, focusing specifically on the integration of reinforcement learning (RL) and Bayesian networks. In the context of complex and dynamic environments, traditional decision-making models often fall short due to their inability to adapt and learn from new data. This study proposes a novel framework that combines the adaptive capabilities of reinforcement learning with the probabilistic reasoning and uncertainty management offered by Bayesian networks. By doing so, it aims to create a robust AI support system that can continuously improve decision-making through interaction with its environment. The research methodology involves the development of a hybrid model that utilizes RL algorithms to optimize decision policies and Bayesian networks to update beliefs and handle uncertainty. Experiments conducted in simulated environments demonstrate the system's ability to achieve superior decision quality compared to conventional methods. The proposed system not only adapts to changing conditions but also provides interpretable insights into the decision-making process, enhancing transparency and trustworthiness. This paper contributes to the field by presenting a scalable solution that can be applied across various domains, including healthcare, finance, and autonomous systems, to support human decision-makers in making informed and optimal choices. The findings suggest significant potential for AI-enhanced systems to transform decision-making, enabling more effective and efficient outcomes in real-world applications.