Problem Solving in Artificial Intelligence

This course will teach you about the basic problem-solving and search algorithms used in Artificial Intelligence (AI). They will learn how to model difficult problems and use uninformed and informed search methods to find good solutions. Uninformed search methods, like Breadth-First Search and Depth-First Search, will be presented as organized ways to look into problem spaces without knowing much about them beforehand. On the other hand, smart search algorithms like A* and Greedy Best-First Search will show how rules can help people solve problems quickly. Constraint Satisfaction Problems (CSPs) are also covered in the course. Students learn to use variables, domains, and constraints to model and answer real-world problems. Methods like backtracking, forward checking, and heuristic ordering will be discussed to improve answers. Students will work on real-world problems like pathfinding, scheduling, and optimization while learning how to judge the success of an algorithm in terms of how complete, optimal, and efficient it is. Students will be able to formalize problems, choose the right algorithms, and put AI-based answers into action by the end of the course. This class is great for people who want to learn a lot about AI problem-solving, which is used in robotics, game creation, and systems that make decisions.