FrAppLe: A Framework for Apprenticeship Learning

Human and AI collaboration has evolved drastically over the past decade. Most of the tasks are now partly handled by machines and partly by humans, resulting in human and AI partnership. Humans share their tasks with the AI agents as they are good at performing predictive and repetitive tasks. The tasks assigned to the agents can be dynamic in nature and can change over time. This may lead to acquiring new skills or re-training to improvise on the same task. The developer needs to programme these agents every time for a new domain. In this work, we propose a framework that helps in developing apprentice agents that learn from Human demonstration. Apprenticeship learning is the process of learning by observing an expert performing actions to achieve a specific goal. The agent observes the expert's actions, the sensory inputs and then trains itself using the observed data. We also propose a process to learn and self-diagnose the agent's action for apprenticeship learning. The framework alleviates the need to explicitly program the apprentice agents. We demonstrate our framework on a case study.