This project sits at the intersection of HCI and Human-Like AI models and the goal is to design teachable agents using Hierarchical Task Networks (HTNs) that can learn from humans in a multi-modal and incremental way. These apprentice agents are provided primitive operators that define fundamental knowledge of actions the agent can take. These actions contain conditions that define when the actions can be applied given a particular configuration of the task environment. Learning is achieved by constructing methods that solve tasks, decomposing higher-level tasks into subtasks, and refining the conditions on methods to determine when they can be applied. These agents will serve as the underlying AI and be integrated into other ongoing projects, including our Apprentice Tutors and HMT gaming work.

Relevant Publications

MacLellan, C.J., Koedinger, K.R. Domain-General Tutor Authoring with Apprentice Learner Models. Int J Artif Intell Educ 32, 76–117 (2022).