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.