Scoring systems are commonly seen for platforms in the era of big data. From
credit scoring systems in financial services to membership scores in E-commerce
shopping platforms, platform managers use such systems to guide users towards
the encouraged activity pattern, and manage resources more effectively and more
efficiently thereby. To establish such scoring systems, several "empirical
criteria" are firstly determined, followed by dedicated top-down design for
each factor of the score, which usually requires enormous effort to adjust and
tune the scoring function in the new application scenario. What's worse, many
fresh projects usually have no ground-truth or any experience to evaluate a
reasonable scoring system, making the designing even harder. To reduce the
effort of manual adjustment of the scoring function in every new scoring
system, we innovatively study the scoring system from the preset empirical
criteria without any ground truth, and propose a novel framework to improve the
system from scratch. In this paper, we propose a "counter-empirical attacking"
mechanism that can generate "attacking" behavior traces and try to break the
empirical rules of the scoring system. Then an adversarial "enhancer" is
applied to evaluate the scoring system and find the improvement strategy. By
training the adversarial learning problem, a proper scoring function can be
learned to be robust to the attacking activity traces that are trying to
violate the empirical criteria. Extensive experiments have been conducted on
two scoring systems including a shared computing resource platform and a
financial credit system. The experimental results have validated the
effectiveness of our proposed framework.

我们提出了一种创新的框架，从预设的经验标准出发，通过对抗性学习问题训练，学习出一个适应攻击行为迹象的得分函数，以提高得分系统的鲁棒性。通过在共享计算资源平台和金融信用系统上进行的大量实验证明了我们提出的框架的有效性。

基于对抗强化学习的逆向经验攻击时间相关评分系统

Counter-Empirical Attacking based on Adversarial Reinforcement Learning  for Time-Relevant Scoring System

The relationship between brains and computers is often taken to be merely
metaphorical. However, genuine computational systems can be implemented in
virtually any media; thus, one can take seriously the view that brains
literally compute. But without empirical criteria for what makes a physical
system genuinely a computational one, computation remains a matter of
perspective, especially for natural systems (e.g., brains) that were not
explicitly designed and engineered to be computers. Considerations from real
examples of physical computers-both analog and digital, contemporary and
historical-make clear what those empirical criteria must be. Finally, applying
those criteria to the brain shows how we can view the brain as a computer
(probably an analog one at that), which, in turn, illuminates how that claim is
both informative and falsifiable.

本文讨论了大脑与计算机之间的关系，提出了大脑也可以被视为计算机的观点，并给出了定义和判定计算机实体的经验标准。通过应用这些标准，揭示了大脑作为一种类比于模拟计算机的计算机的信息价值以及可验证性。