Course information

This course introduces students to the principles, models, and algorithms for planning and search under uncertainty in Artificial Intelligence (AI). It explores how uncertainty arises in decision-making, how it can be modeled using probabilistic frameworks such as Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), and how AI systems can make robust, adaptive choices in dynamic and noisy environments. Students will learn theoretical foundations as well as practical methods for applying these concepts in real-world contexts, such as robotics, game AI, and autonomous systems. The course emphasizes hands-on practice with no-code and low-code tools for modeling, simulation, and experimentation.