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吴泊逾 等: 干扰惰性序列的连续决策模型模糊测试                                                        4659


                 [28]   Ho J, Ermon S. Generative adversarial imitation learning. In: Proc. of the 30th Int’l Conf. on Neural Information Processing Systems.
                     Barcelona: Curran Associates Inc., 2016. 4572–4580.
                 [29]   Shin M, Kim J. Randomized adversarial imitation learning for autonomous driving. In: Proc. of the 28th Int’l Joint Conf. on Artificial
                     Intelligence. Macao: ijcai.org, 2019. 4590–4596. [doi: 10.24963/ijcai.2019/638]
                 [30]   Chen D, Zhou B, Koltun V, Krähenbuhl P. Learning by cheating. In: Proc. of the 3rd Annual Conf. on Robot Learning. Osaka: PMLR,
                     2019. 66–75.
                 [31]   Busoniu L, Babuska R, De Schutter B. A comprehensive survey of multiagent reinforcement learning. IEEE Trans. on Systems, Man, and
                     Cybernetics, Part C (Applications and Reviews), 2008, 38(2): 156–172. [doi: 10.1109/TSMCC.2007.913919]
                 [32]   Vinyals O, Babuschkin I, Czarnecki WM, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 2019,
                     575(7782): 350–354. [doi: 10.1038/s41586-019-1724-z]
                 [33]   Wiering M. Multi-agent reinforcement leraning for traffic light control. In: Proc. of the 17th Int’l Conf. on Machine Learning. Stanford:
                     Morgan Kaufmann Publishers Inc., 2000. 1151–1158.
                 [34]   Bohme M, Pham VT, Roychoudhury A. Coverage-based greybox fuzzing as Markov chain. IEEE Trans. on Software Engineering, 2019,
                     45(5): 489–506. [doi: 10.1109/TSE.2017.2785841]
                 [35]   Franceschi JY, Dieuleveut A, Jaggi M. Unsupervised scalable representation learning for multivariate time series. In: Proc. of the 33rd Int’l
                     Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 418.
                 [36]   van den Oord A, Dieleman S, Zen HG, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K. WaveNet: A
                     generative model for raw audio. In: Proc. of the 9th ISCA Speech Synthesis Workshop. Sunnyvale: ISCA, 2016. 125.
                 [37]   McInnes L, Healy J, Astels S. hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2017, 2(11): 205. [doi:
                     10.21105/joss.00205]
                 [38]   Kim J, Feldt R, Yoo S. Guiding deep learning system testing using surprise adequacy. In: Proc. of the 41st IEEE/ACM Int’l Conf. on
                     Software Engineering. Montreal: IEEE, 2019. 1039–1049. [doi: 10.1109/ICSE.2019.00108]
                 [39]   Feng Y, Shi QK, Gao XY, Wan J, Fang CR, Chen ZY. DeepGini: Prioritizing massive tests to enhance the robustness of deep neural
                     networks. In: Proc. of the 29th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. ACM, 2020. 177–188. [doi: 10.1145/
                     3395363.3397357]
                 [40]   Dosovitskiy A, Ros G, Codevilla F, Lopez A, Koltun V. CARLA: An open urban driving simulator. In: Proc. of the 1st Annual Conf. on
                     Robot Learning. Mountain View: PMLR, 2017. 1–16.
                 [41]   Toromanoff M, Wirbel E, Moutarde F. End-to-end model-free reinforcement learning for urban driving using implicit affordances. In:
                     Proc.  of  the  2020  IEEE/CVF  Conf.  on  Computer  Vision  and  Pattern  Recognition.  Seattle:  IEEE,  2020.  7151–7160.  [doi:  10.1109/
                     CVPR42600.2020.00718]
                 [42]   The CARLA autonomous driving challenge. 2024. https://carlachallenge.org/
                 [43]   CARLA autonomous driving leaderboard. 2024. https://leaderboard.carla.org/leaderboard/
                 [44]   Kuznetsov A, Shvechikov P, Grishin A, Vetrov D. Controlling overestimation bias with truncated mixture of continuous distributional
                     quantile critics. In: Proc. of the 37th Int’l Conf. on Machine Learning. PMLR, 2020. 5556–5566.
                 [45]   Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I. Multi-agent actor-critic for mixed cooperative-competitive environments. In:
                     Proc. of the 31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6382–6393.
                 [46]   Klees G, Ruef A, Cooper B, Wei SY, Hicks M. Evaluating fuzz testing. In: Proc. of the 2018 ACM SIGSAC Conf. on Computer and
                     Communications Security. Toronto: ACM, 2018. 2123–2138. [doi: 10.1145/3243734.3243804]
                 [47]   McLachlan GJ, Basford KE. Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker, 1988.
                 [48]   van der Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(86): 2579–2605.

                             吴泊逾(1990-), 男, 博士生, 主要研究领域为自                 王亚文(1993-), 男, 博士, 助理研究员, 主要研
                            动驾驶系统测试.                                     究领域为智能软件测试, 智能模型对抗攻击.




                             王凯锐(1999-), 男, 硕士, 主要研究领域为智能                 王俊杰(1987-), 女, 博士, 研究员, CCF  专业会

                            软件工程, 智能体测试.                                 员, 主要研究领域为智能软件工程, 软件工程大

                                                                         数据, 经验软件工程, 软件质量, 众包软件测试.
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