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Comprehensive Adversarial search methods Tools for Every Need

Get access to Adversarial search methods solutions that address multiple requirements. One-stop resources for streamlined workflows.

Adversarial search methods

  • AIpacman

    AIpacman is a Python framework providing search-based, adversarial, and reinforcement learning agents to master the Pac-Man game.
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    What is AIpacman?
    AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.
    AIpacman Core Features
    • Search-based agents: DFS, BFS, UCS, A*
    • Adversarial agents: Minimax, Alpha-Beta, Expectimax
    • Reinforcement learning: Q-Learning
    • Configurable maze layouts
    • Game visualization and rendering
    • Performance logging and metrics
    • CLI-driven execution

  • MultiAgentPacman

    Open-source framework enabling implementation and evaluation of multi-agent AI strategies in a classic Pacman game environment.
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    What is MultiAgentPacman?
    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.
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