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Distributed augmentation-based learning: a learning algorithm for distributed collaborative programming-by-demonstration

Published: 28 January 2007 Publication History

Abstract

The learning algorithms used in Programming-by-Demonstration (PBD) are either on-line and incremental or off-line and batch. Neither category is entirely suitable for capturing know-how from demonstrations in a distributed, collaborative environment, where multiple experts can independently provide examples to improve the model.In this paper we describe Distributed Augmentation-Based Learning (DABL), the first real-time PBD learning algorithm suited for distributed know-how acquisition. DABL is an incremental learning algorithm that uses a version-control-like paradigm to combine independently constructed procedure models. An expert can check out a procedure model from a repository and modify it by means of new demonstrations or by manually editing it. The expert then reconciles the changes with those concurrently made by other experts and checked into the repository.DABL automatically merges the two procedures, learns new decision points based on reconcilable differences, and identifies conflicts where there are multiple valid ways of combining the changes or where the combination produces an invalid model, that is, one that does not lie in the search space of the learning algorithm.

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Cited By

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  • (2010)Sheepdog, parallel collaborative programming-by-demonstrationKnowledge-Based Systems10.1016/j.knosys.2009.06.00823:2(94-109)Online publication date: 1-Mar-2010
  • (2008)Recovering from errors during programming by demonstrationProceedings of the 13th international conference on Intelligent user interfaces10.1145/1378773.1378794(159-168)Online publication date: 13-Jan-2008
  • (2007)Augmentation-Based Learning combining observations and user edits for Programming-by-DemonstrationKnowledge-Based Systems10.1016/j.knosys.2007.04.00720:6(575-591)Online publication date: 1-Aug-2007

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  1. Distributed augmentation-based learning: a learning algorithm for distributed collaborative programming-by-demonstration

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    cover image ACM Conferences
    IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces
    January 2007
    388 pages
    ISBN:1595934812
    DOI:10.1145/1216295
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 28 January 2007

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    1. artificial intelligence
    2. example-and demonstration-based interfaces
    3. programming-by-demonstration

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    View all
    • (2010)Sheepdog, parallel collaborative programming-by-demonstrationKnowledge-Based Systems10.1016/j.knosys.2009.06.00823:2(94-109)Online publication date: 1-Mar-2010
    • (2008)Recovering from errors during programming by demonstrationProceedings of the 13th international conference on Intelligent user interfaces10.1145/1378773.1378794(159-168)Online publication date: 13-Jan-2008
    • (2007)Augmentation-Based Learning combining observations and user edits for Programming-by-DemonstrationKnowledge-Based Systems10.1016/j.knosys.2007.04.00720:6(575-591)Online publication date: 1-Aug-2007

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