This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriv...
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This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.
Wearable sensing has recently been highly preferred due to its quick and accurate measurement of physiological parameters. These sensors have been devised using various polymers [1], [2] and nanomaterials [3], [4] sui...
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We propose a method of performing variational inference using high speed photonic neural accelerators. This method incurs no slowdown compared to deterministic photonic inference, affecting only the power consumption ...
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Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract...
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Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy data. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) diverse state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states - a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on 20/21 benchmarks, typically by 2-5x, while maintaining a comparable runtime to Behavior Cloning (BC). Copyright 2024 by the author(s)
Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyberphysical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and c...
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Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static...
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This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have...
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A single-stage single-phase dual active bridge (DAB) based solid-state transformer (SST) offers various advantages over its multi-stage counterparts, including higher efficiency and reliability due to fewer components...
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The stochastic uncertainty of wind speed presents a great challenge for achieving reliable power control in wind energy conversion system (WECS). Due to the excellence in handling the uncertainties based on probabilis...
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With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the...
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With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid *** solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency ***,a threephase unbalanced network’s positive sequence fast estimation model is ***,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss ***,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).
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