In this course, we explain heuristic search methods for problem-solving, illustrated with several examples. We begin by defining heuristics and outlining its benefits and drawbacks. Then, you'll learn about the heuristic calculation technique and the components of a heuristic function. After that, we discuss simple hill climbing, one of the easiest approaches for implementing a heuristic, along with its strategies, challenges and variations.

Next, the course moves on to the best-first search algorithm and describes the basic principle underlying it and the algorithm sketch. A heuristic search of a hypothetical state space and a trace of the execution of the best-first search is then explored. Following that, we explore the factors to consider while measuring problem-solving performance and the admissibility of heuristic and shortest paths.

Lastly, we cover the mini-max algorithm for game playing and the steps for its implementation. After that, you'll discover a two-ply mini-max applied to the Tic-Tac-Toe game moves. Finally, we investigate the alpha-beta pruning algorithm used for a hypothetical state space search graph and the issues related to state space representation in problem-solving. Sign up now to expand your understanding of Heuristic search in Artificial Intelligence.