Abstract
Building AI agents can be costly. Consider a question answering agent such as Jill Watson that auto-matically answers students’ questions on the discussion forums of online classes based on their syllabi and other course materials. Training a Jill on the syllabus of a new online class can take a hundred hours or more. Machine teaching – interactive teaching of an AI agent using synthetic data sets – can reduce the training time because it combines the advantages of knowledge-based AI, machine learning using large data sets, and interactive human-in-loop training. We describe Agent Smith, an interactive machine teaching agent that reduces the time taken to train a Jill for a new online class by an order of magnitude.
Agent Smith Machine Teaching for Building Question Answering Agents
