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.
['James F. Allen', 'Nathanael Chambers', 'George Ferguson', 'Lucian Galescu', 'Hyuckchul Jung', 'Mary D. Swift', 'William Taysom']
PLOW: a collaborative task learning agent