Interpretability of decision making methods

Abstract

A method and system of increasing interpretability of decision making methods include a network module providing raw data from an environment to a machine learning (ML) module. In response to the raw data being delivered to the ML module, the ML module generates a trained classifier using the raw data. A pruning module then prunes a plurality of dominant variables, in the sense of being most relevant for the decision made with respect to the classifier, using the trained classifier. The network module then provides the sub-optimal policy to a reinforcement learning (RL) module, where a generated sub-optimal policy is applied to the environment to obtain a dataset by applying the sub-optimal policy and generating a trajectory. The ML module then generates an interpretable set of rules using the generated trajectory.