论文标题
在学习分类器系统中分离规则发现和全球解决方案组成
Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
论文作者
论文摘要
尽管利用数字代理人来支持关键决策，但对这些代理商提出的建议的信任很难实现。但是，从他们的应用中获利至关重要，从而需要对决策过程和模型进行解释。对于许多系统，例如共同的黑框模型，至少可以实现一些解释性，需要复杂的后处理，而其他系统则从合理的程度上从固有的易于解释。我们提出了一个基于规则的学习系统，专门概念化，因此特别适合这些情况。它的模型本质上是透明的，可以通过设计易于解释。我们系统的一个关键创新是，规则的条件和哪些规则构成问题解决方案是分开进化的。我们利用独立的规则健身，使用户可以专门定制其模型结构，以适合给定的解释性要求。
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for both the decision making process and the model. For many systems, such as common black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. We propose a rule-based learning system specifically conceptualised and, thus, especially suited for these scenarios. Its models are inherently transparent and easily interpretable by design. One key innovation of our system is that the rules' conditions and which rules compose a problem's solution are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability.
