AI Search Algorithms and Problem-Solving Strategies

Every intelligent agent, from pathfinding systems to game-playing programs, relies on search algorithms to navigate choices and find the best possible outcome. Understanding these search strategies is the foundational step to mastering how artificial intelligence makes decisions. This text-based course guides you from absolute beginner concepts to a solid grasp of how AI search algorithms operate, enabling you to read, analyze, and design efficient problem-solving models. What you'll learn: - Understand foundational AI concepts, including state spaces, transition models, and path costs. - Compare uninformed search techniques such as Breadth-First Search (BFS) and Depth-First Search (DFS). - Master informed search strategies using heuristics, including Greedy Best-First and A* search. - Analyze adversarial search algorithms like Minimax and alpha-beta pruning for decision-making in competitive environments. - Apply search strategies to modern routing, pathfinding, and agent-based planning scenarios. You will begin with core definitions and state-space representation before progressing through classical search models, heuristic design, and game-playing algorithms. Through clear written explanations and step-by-step logic walkthroughs, you will develop a strong intuition for algorithmic efficiency. This course is designed for beginners in computer science and artificial intelligence, requiring no advanced programming experience or prior AI knowledge. Start reading today to unlock the core problem-solving techniques that power modern artificial intelligence.