Open Access
Issue
Security and Safety
Volume 4, 2025
Article Number 2024023
Number of page(s) 15
Section Software Engineering
DOI https://doi.org/10.1051/sands/2024023
Published online 23 July 2025
  1. Cesa-Bianchi N, Lugosi G and Stoltz G. Regret minimization under partial monitoring. Math Oper Res 2006; 31: 562–580. [Google Scholar]
  2. Marden JR. State based potential games. Automatica 2012; 48: 3075–3088. [Google Scholar]
  3. Picheny V, Binois M and Habbal A. A Bayesian optimization approach to find Nash equilibria. J Glob Optim 2019; 73: 171–192. [Google Scholar]
  4. Bichler M, Fichtl M and Oberlechner M. Computing Bayes–Nash equilibrium strategies in auction games via simultaneous online dual averaging. Operat Res 2023; 73: 1102–1127. [Google Scholar]
  5. Wang Z, Shen W and Zuo S. Bayesian nash equilibrium in first-price auction with discrete value distributions. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020, 1458–1466. [Google Scholar]
  6. Ui T. Bayesian Nash equilibrium and variational inequalities. J Math Econ 2016; 63: 139–146. [Google Scholar]
  7. Wu JX. Cyberspace endogenous safety and security. Engineering 2021; 15: 179–185. [Google Scholar]
  8. Zhang XY and Li ZB. Overview on moving target defense technology. Commun Technol 2013; 46: 111–113 [Google Scholar]
  9. Hu Z, Chen P and Zhu M, et al. A co-design adaptive defense scheme with bounded security damages against Heartbleed-like attacks. IEEE Trans Inf Forens Secur 2021; 16: 4691–4704. [Google Scholar]
  10. Auer P, Cesa-Bianchi N and Freund Y, et al. The nonstochastic multiarmed bandit problem. SIAM J Comput 2002; 32: 48–77. [Google Scholar]
  11. Uchiya T, Nakamura A and Kudo M. Algorithms for Adversarial Bandit Problems with Multiple Plays Conference on Learning Theory. ALT 2010; 375–389. [Google Scholar]
  12. Seldin Y and Lugosi G. An improved parametrization and analysis of the EXP3++ algorithm for stochastic and adversarial bandits. Conference on Learning Theory, PMLR, 2017, 1743–1759. [Google Scholar]
  13. Lanctot M, Zambaldi V and Gruslys A, et al. A unified game-theoretic approach to multiagent reinforcement learning. Adv. Neural Inf Proc Syst 2017; 30. [Google Scholar]
  14. Gajane P, Urvoy T and Clérot F. A relative exponential weighing algorithm for adversarial utility-based dueling bandits. In: International Conference on Machine Learning, PMLR, 2015, 218–227. [Google Scholar]
  15. Bistritz I, Zhou Z and Chen X, et al. Online EXP3 learning in adversarial bandits with delayed feedback. Adv Neural Inf Proc Syst 2019; 32. [Google Scholar]
  16. Seldin Y, Szepesvári C and Auer P, et al. Evaluation and analysis of the performance of the EXP3 algorithm in stochastic environments. In: European Workshop on Reinforcement Learning, PMLR, 2013, 103–116. [Google Scholar]
  17. Allesiardo R and Féraud R. EXP3 with drift detection for the switching bandit problem. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2015, 1–7. [Google Scholar]
  18. Gutowski N, Amghar T and Camp O, et al. Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma. Inf Sci 2021; 546: 378–396. [Google Scholar]

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