This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing behavior modeling into prediction and learning, this survey meticulously examines the current landscape of research in each domain, shedding light on the latest advancements and methodologies driven by GANs. Furthermore, the paper offers insights into both the theoretical underpinnings and practical implications of GANs in behavior modeling for complex intelligent systems, and proposes potential future research directions to advance the field. Overall, this comprehensive survey serves as a valuable resource for researchers, practitioners, and scholars seeking to deepen their understanding of behavior modeling using GANs and to chart a course for future exploration and innovation in this dynamic field.

中文翻译：

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基于生成对抗网络的复杂智能系统行为建模综述


本文对复杂智能系统的行为建模进行了广泛而深入的调查，特别关注生成对抗网络（GAN）的创新应用。该调查不仅深入研究了 GAN 的基本原理，还阐明了它们在准确建模复杂智能系统所表现出的行为方面的关键作用。通过将行为建模分为预测和学习，这项调查仔细研究了每个领域的当前研究状况，揭示了 GAN 驱动的最新进展和方法。此外，本文还深入探讨了 GAN 在复杂智能系统行为建模中的理论基础和实际意义，并提出了推动该领域发展的潜在未来研究方向。总体而言，这项综合调查对于研究人员、从业者和学者来说是宝贵的资源，他们希望加深对使用 GAN 进行行为建模的理解，并为这一动态领域的未来探索和创新制定路线。