Salem, NemaHaneya, HalaBalbaid, HaninAsrar, Manal2024-02-042024-02-042024http://hdl.handle.net/20.500.14131/1396Artificial Intelligence (AI) has become an integral part of our lives, finding applications across various industries. Search algorithms play a crucial role in AI. This paper focuses on the comparison of different search algorithms within the context of path-planning in the UC Berkeley’s PAC-Man’s game. The algorithms under consideration include Depth-First Search (DFS), Breadth-First Search (BFS), Uniform Cost Search (UCS), Iterative Deepening Depth First Search (IDDFS), and A∗ Search. The objective is to identify the most effective algorithm in terms of path-finding performance. The study’s findings reveal that the A∗ search algorithm outperforms the others in terms of score, cost, and node expansion, making it the most suitable choice for finding the shortest path in the PAC-Man’s game.Artificial Intelligence, Search Algorithms, Path Finding, PAC-MAN GameSMART