Towards explaining blackbox models using genetic network programming : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Computer Science at Massey University, Albany, New Zealand

Abstract

With the emergence of deep learning systems capable of learning intricate robot manoeuvres, control, team coordination, and planning both from data and through inter action with the environment, numerous complex and non-linear problems have found solutions. However, these systems often function as ’black boxes,’ lacking the ability to provide human-interpretable solutions. This study addresses the interpretability challenge in the field of Explainable AI by employing a ’black box’ system as a target model and subsequently transforming it into a computational graph, equivalent to a Genetic Network Program (GNP). Furthermore, we present a methodology for refining and reducing the size of the GNP solution. Lastly, we also test if we could utilize the black box system as a guide to the fitness function in a GNP architecture. To illustrate its efficacy, we use a multi-goal path-finding problem from the OpenAI Gym framework. The experimental results demonstrate the efficacy of the converted and refined GNP solution in successfully addressing the taxi problem across 500 environments, constituting a comprehensive dataset. Notably, the refined GNP solution exhibits no redundant or unnecessary nodes. Despite the research’s focused scope, centering on a single agent with multiple goals, the algorithms introduced in this study lay the groundwork for the development of more sophisticated and interpretable algorithms. These advancements are poised to tackle more intricate challenges in the future.