Course Goals and Description
The course will be dedicated to understanding thecomputational aspects of sequential decision making in AI. The focus will be onpromoting efficient behaviors of agents that need to accomplish complex tasksin uncertain environments.
Wewill learn various AI approaches to sequential decision making, including automated planning, for settings in which the model of the underlying environment is known, and reinforcement learning (RL), for settings in which the model is only partially known.
Thecourse will include learningthe theoretical aspects of various AI approaches, as well a practical evaluatoin of the learned approachesin different environments.

Learning Outcomes : At the end of the course the students will be able to:
– 1. Knowledge of various AI frameworks for modeling AI systems, with a focus on planning and reinforcement learning.
– 2. Understanding the theoretical guarantees and limitations of different AI algorithms.
– 3. Acquiring practical experience using AI tools and implementing them in various AI domains.