AI-Games: A Collection of Core AI Algorithms and Data Structures Implemented in Python

AI-Games is a collection of core AI algorithms and data structures implemented in Python, covering search algorithms, game trees, machine learning basics, reinforcement learning, and other content. The project uses games as a carrier to help AI learners understand abstract algorithms through intuitive scenarios. The code is concise and clear with few dependencies, making it suitable for beginners to learn systematically and for developers as a reference.

AI learning often faces the challenge of disconnect between theoretical abstraction and engineering practice. Games have clear rules, controllable state spaces, and distinct win/loss criteria, making them an excellent carrier for understanding AI algorithms. AI-Games is built on this concept, choosing Python for its balance of readability and practicality, relying only on standard libraries and a few common libraries (such as NumPy) to lower the learning threshold.

The project includes multiple types of core algorithms: 1. Search algorithms (uninformed search such as BFS/DFS/IDS, heuristic search such as A*, adversarial search such as Minimax/Alpha-Beta pruning); 2. Game theory and game AI design (evaluation function design, adaptation to different game types); 3. Constraint satisfaction problems (CSP solver combined with AC-3 algorithm) and optimization algorithms (hill-climbing, simulated annealing, genetic algorithms); 4. Machine learning basics (KNN, decision trees, K-means, perceptron/MLP); 5. Reinforcement learning (application of Q-Learning and SARSA in grid worlds).

The project demonstrates algorithm effects through multiple game scenarios: maze solving to compare performance differences between BFS/DFS/A*; board games (such as Tic-Tac-Toe) implementing Minimax and Alpha-Beta pruning; Sudoku/eight queens problem using CSP solvers; Traveling Salesman Problem (TSP) comparing local search algorithms; MNIST recognition using MLP; grid world games applying Q-Learning for policy learning. These examples help learners intuitively understand algorithm principles.

AI-Games provides learners with transition resources from theory to practice. The code exposes core logic, allowing learners to debug and observe algorithm processes (such as decision tree splitting), modify parameters (such as KNN distance metrics), and deepen their understanding. The project is suitable for both beginners to build a foundation and experienced developers to use as reference implementations, making it a practical tool for AI learning.

Recommended learning path: Start with search algorithms → game tree algorithms → constraint satisfaction and optimization → machine learning → reinforcement learning. Practical suggestions: Design visual interfaces for algorithms; organize algorithm competitions to compare performance; extend code to add new games; read papers to understand differences in industrial-grade implementations.

Project summary: AI-Games helps learners master core AI algorithms and cultivate problem-solving abilities through game carriers. Expansion directions: Integrate deep learning frameworks to implement end-to-end learning for complex games; add multi-agent games such as poker/mahjong; develop visual tutorials; build algorithm performance evaluation benchmarks. The integration of games and AI will continue to drive innovation.