PLOW: A Collaborative Task Learning Agent

To be effective, an agent that collaborates with humans needs to be able to learn new tasks from humans they work with. This paper describes a system that learns executable task models from a single collaborative learning session consisting of demonstration, explanation and dialogue. To accomplish this, the system integrates a range of AI technologies: deep natural language understanding, knowledge representation and reasoning, dialogue systems, planning/agent-based systems and machine learning. A formal evaluation shows the approach has great promise.

Authors

• James Allen
• George Ferguson University of Rochester
• Hyuckchul Jung Mary Swift

Keywords

No keywords are indexed for this paper.