A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation

Most realistic environments are complex, partially observable and impose real-time constraints on agents operating within them. This paper describes a framework that allows agents to learn by observation in such environments. When learning by observation, agents observe an expert performing a task and learn to perform the same task based on those observations. Our framework aims to allow agents to learn in a variety of domains (physical or virtual) regardless of the behaviour or goals of the observed expert. To achieve this we ensure that there is a clear separation between the central reasoning system and any domain-specific information. We present case studies in the domains of obstacle avoidance, robotic arm control, simulated soccer and Tetris.