Azul Game AI Agent is a specialized AI solution for the Azul board game competition. Implemented in Python, it models game state, applies Minimax search for deterministic pruning, and leverages Monte Carlo Tree Search to explore stochastic outcomes. The agent uses custom heuristics to evaluate board positions, prioritizing tile placement patterns that yield high points. It supports head-to-head tournament mode, batch simulations, and result logging for performance analysis. Users can tweak algorithm parameters, integrate with custom game environments, and visualize decision trees to understand move selection.

MultiAgentPacman offers a Python-based game environment where users can implement, visualize, and benchmark multiple AI agents in the Pacman domain. It supports adversarial search algorithms like minimax, expectimax, alpha-beta pruning, as well as custom reinforcement learning or heuristic-based agents. The framework includes a simple GUI, command-line controls, and utilities to log game statistics and compare agent performance under competitive or cooperative scenarios.