Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability
Murali Krishna Pasupuleti · 2025
Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-making, emphasizing the trade-offs between sample efficiency, generalization, and computational complexity. Special attention is given to uncertainty quantification, explainability techniques, and ethical AI, ensuring AI models remain transparent, accountable, and risk-aware. By integrating probabilistic reasoning, deep learning, and structured decision models, this research highlights how AI can make reliable, interpretable, and human-aligned decisions in complex, high-stakes environments. The findings underscore the importance of hybrid AI frameworks, explainable probabilistic models, and uncertainty-aware optimization, shaping the future of trustworthy, scalable, and ethically responsible AI-driven decision-making. Keywords Decision theory, model-based AI, probabilistic learning, Bayesian inference, probabilistic graphical models, reinforcement learning, Markov decision processes, uncertainty quantification, explainable AI, causal inference, model-free learning, Monte Carlo methods, variational inference, hybrid AI frameworks, ethical AI, risk-aware decision-making, optimal control, trust in AI, interpretable machine learning, autonomous systems, financial AI, healthcare AI, AI governance, explainability techniques, real-world AI applications.