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Werkzeuge für akademische Forschung

  • Multi Agent Simulation

    A Python-based framework enabling creation and simulation of AI-driven agents with customizable behaviors and environments.
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    What is Multi Agent Simulation?
    Multi Agent Simulation offers a flexible API to define Agent classes with custom sensors, actuators, and decision logic. Users configure environments with obstacles, resources, and communication protocols, then run step-based or real-time simulation loops. Built-in logging, event scheduling, and Matplotlib integration help track agent states and visualize results. The modular design allows easy extension with new behaviors, environments, and performance optimizations, making it ideal for academic research, educational purposes, and prototyping multi-agent scenarios.
    Multi Agent Simulation Core Features
    • Agent class abstraction with customizable behaviors
    • Environment modeling with obstacles and resources
    • Event-driven simulation loop
    • Inter-agent messaging and communication
    • Logging and performance metrics
    • Matplotlib visualization support

  • RinSim

    RinSim is a Java-based discrete-event multi-agent simulation framework for evaluating dynamic vehicle routing, ride-sharing, and logistics strategies.
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    What is RinSim?
    RinSim provides a modular simulation environment focused on modeling dynamic logistics scenarios with multiple autonomous agents. Users can define road networks via graph structures, configure fleets of vehicles including electric models with battery constraints, and simulate stochastic request arrivals for pickup and delivery tasks. The discrete-event architecture ensures precise timing and event management, while built-in routing algorithms and customizable agent behaviors allow extensive experimentation. RinSim supports output metrics such as travel time, energy consumption, and service level, and includes visualization modules for real-time and post-simulation analysis. Its extensible design enables integration of custom algorithms, scaling up to large fleets, and reproducible research workflows essential for academia and industry optimization of mobility strategies.

  • Summative Info Researcher Agents

    AI agents automating web research, data gathering, and summarization across multiple sources with customizable workflows.
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    What is Summative Info Researcher Agents?
    Summative Info Researcher Agents offers a modular framework of AI-driven agents designed to perform end-to-end research tasks. It automates web searches, scrapes content, extracts relevant data points, and synthesizes findings into clear, structured summaries. Built atop popular LLMs and extensible via plugin tools, the project allows users to define multi-step workflows, chain agents together, and adjust settings for domain-specific queries. Its flexible architecture supports integration with custom APIs, database connectors, and scheduling systems to fit academic, business, or personal research needs.
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