Inferring Affective Consciousness in an Artificial Agent: A Case Study
研究通过构建一个能展示享乐地点偏好的人工代理，探讨其在确定性机制下如何模拟体验不确定性及内在需求，以此探索意识和自由意志的物理基础。
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Selected work
Representative Papers
Technical Report on Resilient and Secure Large-Scale Energy Internet Systems
In the context of deep digitalization, large-scale energy internet systems face significant cyber-physical security and resilience challenges due to the tight coupling among power, information, and market layers. This work proposes a comprehensive modeling and decision-making framework that integrates energy storage coordination, multidimensional resilience, and electricity price forecasting. It introduces a graph-computation-based attack-resilient information routing mechanism and, for the first time, incorporates artificial intelligence trustworthiness assurance into the security and resilience research paradigm for energy internet systems. By synergistically combining cyber-physical system modeling, AI security techniques, and multilayer coordinated control strategies, the project establishes a holistic technical framework tailored to the security and resilience needs of large-scale energy internet infrastructures, thereby providing critical support for standardization efforts and regulatory policy development.
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Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence
This study investigates how local mobility, bounded memory, and network structure jointly shape the critical conditions and time scales under which committed minorities drive shifts in group conventions. To this end, we develop a transparent agent-based model that simulates the dynamics of individuals switching between two behavioral states. Departing from prior work focused solely on whether convention change occurs, this paper systematically links structural and behavioral factors to the temporal dynamics of convention shift and introduces a unified predictive model that quantifies their influence on the time required for full adoption. Simulation results demonstrate that convention change ultimately occurs across most configurations, with mobility acting as the dominant accelerator of convergence; memory length and network connectivity further modulate convergence speed in predictable ways, enabling the model to accurately forecast the time to full adoption.
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Beyond Resilience: Antifragility in Critical Infrastructure Cybersecurity
This study addresses a critical gap in existing cybersecurity frameworks for critical infrastructure, which predominantly emphasize post-disruption recovery while lacking empirical theory on achieving limited improvements during disturbances. The work proposes the first Antifragility Theory (AFT) tailored to operational technology (OT) environments, integrating a five-state resilience system model with a mathematically rigorous definition of Jensen gain. Validation is conducted using a subset of the CISSM incident database and the HAI hardware-in-the-loop experimental platform. The research establishes two necessary conditions for antifragility verification: heterogeneous vulnerability burdens and process-level disturbance observability. Empirical findings reveal that OT systems are particularly susceptible to disruptive attacks, yet exhibit significantly reduced process-state deviations in subsequent observation windows following such attacks, indicating a measurable adaptive response inherent to the system.
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Revisiting Action Factorization for Complex Action Spaces
This study addresses the lack of systematic benchmarks in reinforcement learning for hybrid discrete-continuous action spaces by comprehensively evaluating multiple action factorization approaches—including independent networks, shared encoders, VDN, QPLEX, joint output, and autoregressive methods—across PPO, SAC, and DQN algorithms and three action space configurations. The evaluation spans 220 experimental setups across four lightweight environments. Key contributions include the introduction of two novel variants, VDN-PPO and PPO-MIX, featuring a branched critic architecture; the release of two new Gymnasium/PettingZoo-compliant environments, CoopPush and Hybrid-Shoot; and empirical findings showing that autoregressive methods generally achieve the best performance, native continuous SAC leads in effectiveness at higher computational cost, and the branched dual-stream architecture offers the most favorable trade-off between efficiency and performance.
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Inferring Affective Consciousness in an Artificial Agent: A Case Study
研究通过构建一个能展示享乐地点偏好的人工代理，探讨其在确定性机制下如何模拟体验不确定性及内在需求，以此探索意识和自由意志的物理基础。
0 citationsRead paper
Technical Report on Resilient and Secure Large-Scale Energy Internet Systems
In the context of deep digitalization, large-scale energy internet systems face significant cyber-physical security and resilience challenges due to the tight coupling among power, information, and market layers. This work proposes a comprehensive modeling and decision-making framework that integrates energy storage coordination, multidimensional resilience, and electricity price forecasting. It introduces a graph-computation-based attack-resilient information routing mechanism and, for the first time, incorporates artificial intelligence trustworthiness assurance into the security and resilience research paradigm for energy internet systems. By synergistically combining cyber-physical system modeling, AI security techniques, and multilayer coordinated control strategies, the project establishes a holistic technical framework tailored to the security and resilience needs of large-scale energy internet infrastructures, thereby providing critical support for standardization efforts and regulatory policy development.
0 citationsRead paper
Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence
This study investigates how local mobility, bounded memory, and network structure jointly shape the critical conditions and time scales under which committed minorities drive shifts in group conventions. To this end, we develop a transparent agent-based model that simulates the dynamics of individuals switching between two behavioral states. Departing from prior work focused solely on whether convention change occurs, this paper systematically links structural and behavioral factors to the temporal dynamics of convention shift and introduces a unified predictive model that quantifies their influence on the time required for full adoption. Simulation results demonstrate that convention change ultimately occurs across most configurations, with mobility acting as the dominant accelerator of convergence; memory length and network connectivity further modulate convergence speed in predictable ways, enabling the model to accurately forecast the time to full adoption.
0 citationsRead paper
Beyond Resilience: Antifragility in Critical Infrastructure Cybersecurity
This study addresses a critical gap in existing cybersecurity frameworks for critical infrastructure, which predominantly emphasize post-disruption recovery while lacking empirical theory on achieving limited improvements during disturbances. The work proposes the first Antifragility Theory (AFT) tailored to operational technology (OT) environments, integrating a five-state resilience system model with a mathematically rigorous definition of Jensen gain. Validation is conducted using a subset of the CISSM incident database and the HAI hardware-in-the-loop experimental platform. The research establishes two necessary conditions for antifragility verification: heterogeneous vulnerability burdens and process-level disturbance observability. Empirical findings reveal that OT systems are particularly susceptible to disruptive attacks, yet exhibit significantly reduced process-state deviations in subsequent observation windows following such attacks, indicating a measurable adaptive response inherent to the system.
0 citationsRead paper
Revisiting Action Factorization for Complex Action Spaces
This study addresses the lack of systematic benchmarks in reinforcement learning for hybrid discrete-continuous action spaces by comprehensively evaluating multiple action factorization approaches—including independent networks, shared encoders, VDN, QPLEX, joint output, and autoregressive methods—across PPO, SAC, and DQN algorithms and three action space configurations. The evaluation spans 220 experimental setups across four lightweight environments. Key contributions include the introduction of two novel variants, VDN-PPO and PPO-MIX, featuring a branched critic architecture; the release of two new Gymnasium/PettingZoo-compliant environments, CoopPush and Hybrid-Shoot; and empirical findings showing that autoregressive methods generally achieve the best performance, native continuous SAC leads in effectiveness at higher computational cost, and the branched dual-stream architecture offers the most favorable trade-off between efficiency and performance.
0 citationsRead paper