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    Dynamic Resource Allocation in Systems-of-Systems Using a Heuristic-Based Interpretable Deep Reinforcement Learning 

    Source: Journal of Mechanical Design:;2022:;volume( 144 ):;issue: 009:;page 91711
    Author(s): Chen, Qiliang;Heydari, Babak
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Systems-of-systems (SoS) often include multiple agents that interact in both cooperative and competitive modes. Moreover, they involve multiple resources, including energy, information, and bandwidth. If these resources ...
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    Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach 

    Source: Journal of Computing and Information Science in Engineering:;2025:;volume( 025 ):;issue: 006:;page 61006-1
    Author(s): Chen, Qiliang; Heydari, Babak
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Effective governance and steering of behavior in complex multiagent systems (MAS) are essential for managing system-wide outcomes, particularly in environments where interactions are structured by dynamic networks. In many ...
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    Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation 

    Source: Journal of Mechanical Design:;2021:;volume( 143 ):;issue: 007:;page 071701-1
    Author(s): Chen, Qiliang; Heydari, Babak; Moghaddam, Mohsen
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The vision of Industry 4.0 is to materialize the notion of a lot-size of one through enhanced adaptability and resilience of manufacturing and logistics operations to dynamic changes or deviations on the shop floor. This ...
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