Artificial intelligence (AI) techniques are increasingly being applied to solve control problems. However, control systems developed in AI are often black-box methods, in that it is not clear how and why they generate their outputs. A lack of transparency can be problematic for control tasks in particular, because it complicates the identification of biases or errors, which in turn negatively influences the user's confidence in the system. To improve the interpretability and transparency in control systems, the black-box structure can be replaced with white-box symbolic policies described by mathematical expressions. Genetic programming offers a gradient-free method to optimise the structure of non-differentiable mathematical expressions. In this paper, we show that genetic programming can be used to discover symbolic control systems. This is achieved by learning a symbolic representation of a function that transforms observations into control signals. We consider both systems that implement static control policies without memory and systems that implement dynamic memory-based control policies. In case of the latter, the discovered function becomes the state equation of a differential equation, which allows for evidence integration. Our results show that symbolic policies are discovered that perform comparably with black-box policies on a variety of control tasks. Furthermore, the additional value of the memory capacity in the dynamic policies is demonstrated on experiments where static policies fall short. Overall, we demonstrate that white-box symbolic policies can be optimised with genetic programming, while offering interpretability and transparency that lacks in black-box models.

通过遗传编程优化代数表达式，我们展示了白盒符号控制系统可用于学习和优化转换观察到的控制信号的函数，从而提供了比黑盒模型更具解释性和透明度的方案。

用遗传规划发现动态符号策略