Extracting Human-readable Knowledge Rules in Complex Time-evolving Environments

A production rule system is a reasoning system that uses rules for knowledge representation. Manual rule acquisition requires a great amount of effort and time from humans. In this paper, we present a data-driven technique for autonomously extracting human-readable rules from complex, time-evolving environments that makes rule acquisition for production rule systems efficient. Complex, time-evolving environments are often highly dynamic and hard to predict. We represent these environments using sets of attributes, and transform those attributes to the frequency domain which enables analysis to extract important features. We extract human-readable knowledge rules from these features using rule-based classification techniques and translating the decision rules back to the time domain. We present an evaluation of our methodology on three environments: hurricane data, a real-time strategy game, and a currency exchange. Experiments show extracted rules are humanreadable and achieve good prediction accuracy.