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Play-by-Play Learning for Textual User Interfaces
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Author(s): Nate Blaylock (Florida Institute for Human and Machine Cognition, USA), William de Beaumont (Florida Institute for Human and Machine Cognition, USA), Lucian Galescu (Florida Institute for Human and Machine Cognition, USA), Hyuckchul Jung (Florida Institute for Human and Machine Cognition, USA), James Allen (University of Rochester, USA), George Ferguson (University of Rochester, USA)and Mary Swift (University of Rochester, USA)
Copyright: 2012
Pages: 14
Source title:
Applied Natural Language Processing: Identification, Investigation and Resolution
Source Author(s)/Editor(s): Philip M. McCarthy (The University of Memphis, USA)and Chutima Boonthum-Denecke (Hampton University, USA)
DOI: 10.4018/978-1-60960-741-8.ch020
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Abstract
This chapter describes a dialog system for task learning and its application to textual user interfaces. Our system, PLOW, uses observation of user demonstration, together with the user’s play-by-play description of that demonstration, to learn complex tasks. We describe some preliminary experiments which show that this technique may make it possible for users without any programming experience to create tasks via natural language.
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