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Responding to and Recovering from Mistakes during Collaboration

Learn how to respond to and recover from mistakes during collaboration, including identifying intentions behind errors, adding recovery goals to shared plans, and generating mutual understanding. Discover error-recovery criteria and practical strategies for successful task-oriented human collaboration.

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Responding to and Recovering from Mistakes during Collaboration

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  1. Responding to and Recovering from Mistakes during Collaboration Andrew Garland, Neal Lesh, and Charles Rich Mitsubishi Electric Research Laboratories

  2. Introduction • Successful collaboration requires that the human and the system each maintain an up-to-date model of the shared plans and goals • What to do if the human makes a mistake or interrupts the activity (called non-contributors)? • Infer intentions underlying mistakes • Repair shared plan by adding recovery goals • Generate utterances that (re-)establish mutual understanding about non-contributors and recoveries

  3. Trace snippet involving recovery User presses the engage button. (mistake) Agent says “Whoops, it was too soon to press the engage button.” Agent says “Let’s recover from pressing the engage button.” Agent says “First, let’s set the engine speed to zero” ... Agent says “We’ve recovered from pressing the engage button.” Agent says “Let’s return to starting the engine”

  4. Two error-recovery criteria • An agent must be able to indicate when recovery is needed, and which actions are part of the recovery • Re-use as much of general-purpose capabilities as possible, e.g. when • selecting method to achieve recovery • deciding whether to take the initiative • generating agent responses

  5. Task-Oriented Human Collaboration Collagen Collagen focus stack plan tree communicate observe observe interact interact

  6. after user mistake RF e d [A] Example – current discourse state A B e c d [B] [A] c Plan tree Focus Stack

  7. Related research • Plan repair and adaptation (Alterman ’88, Hammond ’90, Hanks & Weld ’95, Nebel & Koehler ’95) • Intelligent tutoring systems (Conati et al. ’95, Rickel & Johnson ’99, Rickel et al. ’01) • Probabilistic plan recognition (Bauer et al. ’93, Charniak & Goldman ’93)

  8. Summary • Non-contributors are identified by relaxing constraints during plan recognition (part of discourse interpretation) • Utterances are used to (re-)establish mutual belief about: • When recovery is needed • Which actions are part of recovery • Implementation re-uses many components that were built to handle “normal” behavior

  9. Responding to and Recovering from Mistakes during Collaboration Andrew Garland, Neal Lesh, and Charles Rich Cambridge Research Lab Mitsubishi Electric Research Laboratories

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