Lisa Torrey
| Summary | Apply five core AI algorithms to five classic puzzles and games within a consistent Python framework. |
| Topics | Breadth-first search, A* search, simulated annealing, minimax, and Q-learning. |
| Audience | Undergraduate introduction to artificial intelligence. |
| Difficulty | These are intended to be small programming exercises to demonstrate core algorithms. As examples they should fit within one class period, and as assignments they should fit within one week. |
| Strengths | Working on a range of popular problems keeps students interested, but adopting a consistent approach reduces the cognitive load. Console-based animation allows for visualization without the overhead of a graphics library. |
| Weaknesses | Using classic domains always poses some risk of internet plagiarism. |
| Dependencies | Assumes students can work with classes, strings, tuples, lists, sets, and dictionaries in Python. |
| Variants | Provide more or less starter code. Change the application domain for an algorithm. Ask students to compare related algorithms within a domain. Convert the projects into another programming language. |