Separating Rule Discovery and Global Solution Composition in a Learning Classifier System

TL;DR

This paper introduces a rule-based learning system that separates rule discovery from solution composition, enhancing interpretability and allowing tailored explanations for decision-making models.

Contribution

The novel system evolves rules and their composition independently, improving transparency and customization for explainability in decision support applications.

Findings

Models are inherently transparent and interpretable.

Independent rule fitness enables tailored model structures.

System supports explainability without complex post-processing.

Abstract

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.