Multi-Agent-AI-Models-and-Path-Planning provides a comprehensive toolkit for developing and testing multi-agent systems combined with classical and modern path planning methods. It includes implementations of algorithms such as A*, Dijkstra, RRT, and potential fields, alongside customizable agent behavior models. The framework features simulation and visualization modules, allowing seamless scenario creation, real-time monitoring, and performance analysis. Designed for extensibility, users can plug in new planning algorithms or agent decision models to evaluate cooperative navigation and task allocation in complex environments.

multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.

An AI Football Cup in a Java JADE Environment is an open-source demonstration that leverages the Java Agent DEvelopment Framework (JADE) to simulate a full soccer tournament. It models each player as an autonomous agent with behaviors for movement, ball control, passing, and shooting, coordinating via message passing to implement strategies. The simulator includes referee and coach agents, enforces game rules, and manages tournament brackets. Developers can extend decision-making with custom rules or integrate machine learning modules. This environment illustrates multi-agent communication, teamwork, and dynamic strategy planning within a real-time sports scenario.