Evaluating Rule-based Programming and Reinforcement Learning for Personalising an Intelligent System

Many intelligent systems can be personalised by end-users to suit their specific needs. However, the interface for personalisation often trades off the degree of personalisation achievable with time, effort, and level of expertise required by the user. We explore two approaches to end-user personalisation: one asks the user to manually specify the system’s desired behaviour using an end-user programming language, while the other only asks the user to provide feedback on the system’s behaviour to train the system using reinforcement learning. To understand the advantages and disadvantages of each approach, we conducted a comparative user study. We report participant attitudes towards each and discuss the implications of choosing one over the other.