• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 32 matches

• 2024
• 
  Mirco Theile:
  Modeling Planning, Control, and Scheduling of Cyber-Physical Systems for Reinforcement Learning. Technical University of Munich, Germany, 2024
• 
  Xue Yang, Enda Howley, Michael Schukat:
  ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning. Comput. Secur. 141: 103825 (2024)
• 
  Fatima Ezzahra Achamrah, Ali Attajer:
  Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems. Int. J. Prod. Res. 62(10): 3460-3482 (2024)
• 
  Xinge Li, Xiaoya Hu, Tao Jiang:
  Dual-Reinforcement-Learning-Based Attack Path Prediction for 5G Industrial Cyber-Physical Systems. IEEE Internet Things J. 11(1): 50-58 (2024)
• 
  Shixiong Jiang, Mengyu Liu, Fanxin Kong:
  Vulnerability Analysis for Safe Reinforcement Learning in Cyber-Physical Systems. ICCPS 2024: 77-86
• 
  Shixiong Jiang, Mengyu Liu, Fanxin Kong:
  Demo: Vulnerability Analysis for STL-Guided Safe Reinforcement Learning in Cyber-Physical Systems. RTAS 2024: 400-401
• 2023
• 
  Zhaojun Hao, Francesco Di Maio, Enrico Zio:
  A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply. Reliab. Eng. Syst. Saf. 235: 109231 (2023)
• 
  Yan Yu, Wen Yang, Wenjie Ding, Jiayu Zhou:
  Reinforcement Learning Solution for Cyber-Physical Systems Security Against Replay Attacks. IEEE Trans. Inf. Forensics Secur. 18: 2583-2595 (2023)
• 
  Alberto Robles Enciso, Ricardo Robles-Enciso, Antonio F. Skarmeta:
  Multi-agent Reinforcement Learning-Based Energy Orchestrator for Cyber-Physical Systems. ALGOCLOUD 2023: 100-114
• 
  Elias Modrakowski, Niklas Braun, Mehrnoush Hajnorouzi, Andreas Eich, Narges Javaheri, Richard Doornbos, Sebastian Moritz, Jan-Willem Bikker, Rutger van Beek:
  Architecture for Digital Twin-Based Reinforcement Learning Optimization of Cyber-Physical Systems. ECSA (Tracks, Workshops and Doctoral Symposium) 2023: 257-271
• 
  Zhengcheng Dong, Mian Tang, Meng Tian:
  Allocating defense resources for spatial cyber-physical power systems based on deep reinforcement learning. ICPS 2023: 1-6
• 
  Xin Qin, Nikos Aréchiga, Jyotirmoy Deshmukh, Andrew Best:
  Robust Testing for Cyber-Physical Systems using Reinforcement Learning. MEMOCODE 2023: 36-46
• 
  Hyunsoo Lee, Soohyun Park, Won Joon Yun, Soyi Jung, Joongheon Kim:
  Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems. CoRR abs/2301.00124 (2023)
• 
  (Withdrawn) Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems. CoRR abs/2302.01567 (2023)
• 2022
• 
  Minrui Xu, Jialiang Peng, Brij B. Gupta, Jiawen Kang, Zehui Xiong, Zhenni Li, Ahmed A. Abd El-Latif:
  Multiagent Federated Reinforcement Learning for Secure Incentive Mechanism in Intelligent Cyber-Physical Systems. IEEE Internet Things J. 9(22): 22095-22108 (2022)
• 
  J. Stanly Jayaprakash, M. Jasmine Pemeena Priyadarsini, Parameshachari Bidare Divakarachari, Hamid Reza Karimi, Sasikumar Gurumoorthy:
  Deep Q-Network with Reinforcement Learning for Fault Detection in Cyber-Physical Systems. J. Circuits Syst. Comput. 31(9): 2250158:1-2250158:34 (2022)
• 
  Timothy Rupprecht, Yanzhi Wang:
  A survey for deep reinforcement learning in markovian cyber-physical systems: Common problems and solutions. Neural Networks 153: 13-36 (2022)
• 
  Ryan Silva, Cameron Hickert, Nicolas R. Sarfaraz, Jeff Brush, Josh Silbermann, Tamim Sookoor:
  AlphaSOC: Reinforcement Learning-based Cybersecurity Automation for Cyber-Physical Systems. ICCPS 2022: 290-291
• 
  Eunho Cho, Gwangoo Yeo, Eunkyoung Jee, Doo-Hwan Bae:
  Anomaly-Aware Adaptation Approach for Self-Adaptive Cyber-Physical System of Systems Using Reinforcement Learning. SoSE 2022: 7-12
• 
  Mohamad Chehadeh, Igor Boiko, Yahya H. Zweiri:
  The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems. CoRR abs/2209.15216 (2022)
• 2021
• 
  Shikang Xu, Israel Koren, C. Mani Krishna:
  Adaptive workload adjustment for cyber-physical systems using deep reinforcement learning. Sustain. Comput. Informatics Syst. 30: 100525 (2021)
• 
  Shaohua Zhang, Shuang Liu, Jun Sun, Yuqi Chen, Wenzhi Huang, Jinyi Liu, Jian Liu, Jianye Hao:
  FIGCPS: Effective Failure-inducing Input Generation for Cyber-Physical Systems with Deep Reinforcement Learning. ASE 2021: 555-567
• 2020
• 
  Gwangpyo Yoo, Minjong Yoo, Ikjun Yeom, Honguk Woo:
  rocorl: Transferable Reinforcement Learning-Based Robust Control for Cyber-Physical Systems With Limited Data Updates. IEEE Access 8: 225370-225383 (2020)
• 
  Alex S. Leong, Arunselvan Ramaswamy, Daniel E. Quevedo, Holger Karl, Ling Shi:
  Deep reinforcement learning for wireless sensor scheduling in cyber-physical systems. Autom. 113: 108759 (2020)
• 
  Linfei Yin, Shengyuan Li, Hui Liu:
  Lazy reinforcement learning for real-time generation control of parallel cyber-physical-social energy systems. Eng. Appl. Artif. Intell. 88 (2020)
• 
  Jupiter Bakakeu, Dominik Kißkalt, Jörg Franke, Shirin Baer, Hans-Henning Klos, Jörn Peschke:
  Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems. CCECE 2020: 1-8
• 
  Arnab Bhattacharya, Thiagarajan Ramachandran, Sandeep Banik, Chase P. Dowling, Shaunak D. Bopardikar:
  Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning. ISI 2020: 1-6
• 
  Joseph Khoury, Mohamed Nassar:
  A Hybrid Game Theory and Reinforcement Learning Approach for Cyber-Physical Systems Security. NOMS 2020: 1-9
• 
  Arnab Bhattacharya, Thiagarajan Ramachandran, Sandeep Banik, Chase P. Dowling, Shaunak D. Bopardikar:
  Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning. CoRR abs/2011.04635 (2020)
• 2019
• 
  Xing Liu, Hansong Xu, Weixian Liao, Wei Yu:
  Reinforcement Learning for Cyber-Physical Systems. ICII 2019: 318-327

skipping 2 more matches