دانلود مقاله انگلیسی رایگان:طراحی همکاری برنامه ریزی وظیفه مبتنی بر عامل و یادگیری تقویتی عمیق برای کار گروهی انسان-پهپاد - 2020
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  • Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork
    Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork


    ترجمه فارسی عنوان مقاله:

    طراحی همکاری برنامه ریزی وظیفه مبتنی بر عامل و یادگیری تقویتی عمیق برای کار گروهی انسان-پهپاد


    منبع:

    Sciencedirect - Elsevier - Chinese Journal of Aeronautics, Uncorrected proof. doi:10.1016/j.cja.2020.05.001


    نویسنده:

    Chang WANGa, Lizhen WUa,*, Chao YANa, Zhichao WANGa, Han LONGb, 7 Chao YUc


    چکیده انگلیسی:

    Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods
    KEYWORDS : Coactive design | Deep reinforcement learning | Human-robot teamwork | Mixed-initiative | Multi-agent system | Task planning | UAV


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 16
    حجم فایل: 2889 کیلوبایت

    قیمت: رایگان


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