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.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: James F. Allen, Nathanael Chambers, George Ferguson, Lucian Galescu, Hyuckchul Jung, Mary D. Swift, William Taysom
• Keywords: AAAI 2007, Collaborative Learning Session, Deep Natural Language, Executable Task Models, Intelligent Agents