In this work, we focus on advancing the state of the art in intelligent agents that can learn complex procedural tasks from humans. Our main innovation is to view the interaction between the human and the robot as a mixed- initiative collaboration. Our contribution is to integrate hierarchical task networks and collaborative discourse theory into the learning from demonstration paradigm to enable robots to learn complex tasks in collaboration with the human teacher.

We have developed and demonstrated an experimental authoring and run-time tool, called Disco for Games, that supports the creation of games in which dialogue and action are integrated without the need for changing modes. This tool is based on collaborative discourse theory and hierarchical task networks, in which utterances are treated as actions, and has a number of additional benefits including better modeling of interruptions, automatic dialogue generation, plan recognition and automatic failure retry.