PLOW: a collaborative task learning agent

Authors:
James Allen;Nathanael Chambers;George Ferguson;Lucian Galescu;Hyuckchul Jung;Mary Swift;William Taysom
Affiliations:
Institute for Human and Machine Cognition, Pensacola, FL;Dept. of Computer Science, Stanford University, Stanford, CA;Dept. of Computer Science, University of Rochester, Rochester, NY;Institute for Human and Machine Cognition, Pensacola, FL;Institute for Human and Machine Cognition, Pensacola, FL;Dept. of Computer Science, University of Rochester, Rochester, NY;Institute for Human and Machine Cognition, Pensacola, FL
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

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Abstract

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.