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    Batch versus Interactive Learning by Demonstration

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    Batch versus Interactive Learning by Demonstration.pdf (207.8Kb)
    Date
    2010
    Author
    Zang, Peng
    Tian, Runhe
    Thomaz, Andrea L.
    Isbell, Charles L.
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    Abstract
    Agents that operate in human environments will need to be able to learn new skills from everyday people. Learning from demonstration (LfD) is a popular paradigm for this. Drawing from our interest in Socially Guided Machine Learning, we explore the impact of interactivity on learning from demonstration. We present findings from a study with human subjects showing people who are able to interact with the learning agent provide better demonstrations (in part) by adapting based on learner performance which results in improved learning performance. We also find that interactivity increases a sense of engagement and may encourage players to participate longer. Our exploration of interactivity sheds light on how best to obtain demonstrations for LfD applications.
    URI
    http://hdl.handle.net/1853/38258
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    • Socially Intelligent Machines Lab (SIM) [32]
    • Socially Intelligent Machines Lab (SIM) Publications [32]

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