A framework for teaching and executing verb phrases

Daniel Hewlett, Thomas J. Walsh, Paul Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes a framework for an agent to learn verb-phrase meanings from human teachers and combine these models with environmental dynamics so the agent can enact verb commands from the human teacher. This style of human/agent interaction allows the human teacher to issue natural-language commands and demonstrate ground actions, thereby alleviating the need for advanced teaching interfaces or difficult goal encodings. The framework extends prior work in apprenticeship learning and builds off of recent advancements in learning to recognize activities and modeling domains with multiple objects. In our studies, we show how to both learn a verb model and turn it into reward and heuristic functions that can then be composed with a dynamics model. The resulting "combined model" can then be efficiently searched by a sample-based planner which determines a policy for enacting a verb command in a given environment. Our experiments with a simulated robot domain show this framework can be used to quickly teach verb commands that the agent can then enact in new environments.

Original languageEnglish (US)
Title of host publicationHelp Me Help You
Subtitle of host publicationBridging the Gaps in Human-Agent Collaboration - Papers from the AAAI Spring Symposium, Technical Report
PublisherAI Access Foundation
Number of pages6
ISBN (Print)9781577354970
StatePublished - 2011
Externally publishedYes
Event2011 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 21 2011Mar 23 2011

Publication series

NameAAAI Spring Symposium - Technical Report


Other2011 AAAI Spring Symposium
Country/TerritoryUnited States
CityStanford, CA

ASJC Scopus subject areas

  • Artificial Intelligence


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