Probabilistic Reasoning - Artificial Intelligence

In this lecture from the Graduate Artificial Intelligence course, we dive into the world of probabilistic reasoning—a foundational concept for building intelligent systems that can operate under uncertainty. Up to this point, we’ve explored search algorithms (uninformed, informed, and adversarial), constraint satisfaction problems, Markov Decision Processes (MDPs), and reinforcement learning. While these tools enable agents to make decisions and learn from rewards, a critical question remains: how should an agent reason when new information is observed? This session introduces probability theory as a formal framework for modeling uncertainty, with a focus on how to represent and update beliefs when partial or noisy observations are made. We explain key concepts like outcomes, events, sample spaces, and conditional probability, and show how they lay the groundwork for powerful tools like Bayesian networks and inference algorithms. Whether you're building autonomous robots, medical diagnostic tools, or language models, probabilistic reasoning is an essential ingredient in enabling machines to make informed, data-driven decisions. 📘 Topics Covered: Events vs. Outcomes Observations and Belief Updates Reasoning under Uncertainty For students of AI, data science, and cognitive systems.