Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Adversarial training is an effective method to train deep learning models that are resilient to
norm-bounded perturbations, with the cost of nominal performance drop. While adversarial …

In online social networks (OSNs), detection of malicious social bots is an important research
challenge to provide legitimacy of user profiles and trustworthy service ratings. Further …

摘要在科技创新牵引和管控政策的推动下, 无人机产业焕发出新的活力. 2019 年,
无人机自主控制及应用技术又取得长足发展, 呈现出一些新的发展态势. 从无人机新战略 …

Although the issue of sparse expert samples at the early stage of training in inverse
reinforcement learning (IRL) is successfully resolved by the introduction of generative …

Despite the growing popularity of deep learning technologies, high memory requirements
and power consumption are essentially limiting their application in mobile and IoT areas …

In traditional decision-theoretic planning, information gathering is a means to a goal. The
agent receives information about its environment (state or observation) and uses it as a way …

Even though recent advances in deep learning (DL) led to tremendous improvements for
various computer and robotic vision tasks, existing DL approaches suffer from a significant …

The modeling of opponent deception in an intelligent game system is not sufficient.
However, an opponent agent may launch deceptive actions to consume defense resources …