In this module, you will explore how complex problems can be represented and solved through search. You will learn how to define a search problem by identifying states, actions, goals, and costs, and examine how these components guide an algorithm’s ability to find solutions. Through toy examples and hands-on practice, you will see how problems are structured so that search algorithms can navigate possible paths toward a goal. You will also compare common uninformed search strategies—including breadth-first search (BFS), depth-first search (DFS), and uniform-cost search—to understand how different approaches affect efficiency and performance.

In this module, you will explore informed search methods that improve problem-solving efficiency by guiding algorithms with heuristics. You will examine how objective functions help algorithms prioritize promising paths and learn how strategies such as greedy best-first search and A* search use heuristic information to find solutions more efficiently. You will also investigate conditions for optimality in A*, explore variations such as IDA* and weighted A*, and implement A* in a practical activity. You will also analyze how heuristic quality influences algorithm performance, including runtime complexity and branching behavior. By studying approaches for developing heuristics—such as relaxed problems, sub-problems, and experience-based learning—you will gain insight into how well-designed heuristics can dramatically improve search performance in complex problem spaces.

In this module, you will explore search problems where the objective is to find an optimal state rather than a sequence of actions. Using the classic 8-queens problem, you will examine how local search algorithms such as hill climbing navigate solution spaces and learn key concepts including local and global optima. You will also study strategies that improve local search performance, such as random restarts, sideways moves, and allowing downhill steps to escape local optima. The module introduces broader optimization approaches including simulated annealing, genetic algorithms, beam search, and tabu search, along with nature-inspired algorithms like particle swarm optimization and ant colony optimization, helping you understand how these methods are applied to solve complex real-world optimization problems.

In this module, you will explore how artificial intelligence approaches decision-making in competitive environments through game playing. You will examine the structure of games commonly studied in AI, focusing on two-player, turn-taking games with complete information. The module introduces the minimax algorithm as a method for evaluating possible game outcomes and selecting optimal moves, and explains how alpha-beta pruning improves efficiency by reducing the number of game states that must be evaluated. You will also explore the historical foundations of AI game playing, including early work on computer chess, and review examples of games where AI techniques are applied today.