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
    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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