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RISAdaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach
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 ...
Dynamic Resource Allocation in Systems-of-Systems Using a Heuristic-Based Interpretable Deep Reinforcement Learning
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 ...
Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach
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 ...
Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation
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 ...
Adaptive Information Modulation: Designing Governance Mechanisms for Multi-Agent Artificial Intelligence Systems
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Modern engineered systems increasingly involve complex sociotechnical environments where multiple agents—including humans and the emerging paradigm of agentic AI powered by large language models (LLMs)—must ...