Showing 3 open source projects for "system game"

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  • 1
    Unity MCP

    Unity MCP

    AI-powered bridge connecting LLMs and advanced AI agents

    Unity-MCP is an open-source integration that connects artificial intelligence assistants with the Unity game development environment through the Model Context Protocol. The project enables AI tools such as coding assistants and autonomous agents to interact directly with Unity projects, allowing them to analyze scenes, modify assets, and generate code within the development environment. By exposing Unity editor functionality through MCP tools, the plugin allows external AI systems to understand the structure of a game project and manipulate it programmatically. ...
    Downloads: 8 This Week
    Last Update:
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  • 2
    LLM Colosseum

    LLM Colosseum

    Benchmark LLMs by fighting in Street Fighter 3

    LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. Instead of relying purely on reward signals as in reinforcement learning agents, the models analyze contextual information and generate strategic actions based on the game environment. ...
    Downloads: 0 This Week
    Last Update:
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  • 3
    NBA Sports Betting Machine Learning

    NBA Sports Betting Machine Learning

    NBA sports betting using machine learning

    NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. ...
    Downloads: 0 This Week
    Last Update:
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