A personal Python library for studying and implementing classical Artificial Intelligence algorithms from scratch. Built as a learning framework with clean abstractions, real test coverage, and working examples.
- Uninformed: BFS, DFS
- Informed: A*, UCS (Uniform Cost Search)
- Minimax with Alpha-Beta Pruning
- Backtracking solver
- Examples: Map Coloring, River Crossing, Maze, N-Queens variants
src/pathos/
├── searching/ # BFS, DFS, A*, UCS
├── adversarial/ # Minimax, Alpha-Beta
├── csp/ # CSP core + solvers
├── optimization/ # (in progress)
└── examples/ # Runnable demos
pip install -e .
pytest tests/- Genetic Algorithms (GA)
- Particle Swarm Optimization (PSO)
- Monte Carlo Tree Search (MCTS)
- Differential Evolution & Simulated Annealing
- PyPI release
This is an ongoing personal project — algorithms are added incrementally as I study them. The focus is on clean, well-documented implementations over performance.