Reactive or Proactive? How Robots Should Explain Failures-Reference-Cited by-同舟云学术

Reactive or Proactive? How Robots Should Explain Failures

Published:2024-03-11 Issue: Volume:10 Page:413-422
ISSN:
Container-title:Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
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Author:

LeMasurier Gregory¹^ORCID,Gautam Alvika²^ORCID,Han Zhao³^ORCID,Crandall Jacob W.⁴^ORCID,Yanco Holly A.¹^ORCID

Affiliation:

1. University of Massachusetts Lowell, Lowell, MA, USA

2. Texas A&M University, College Station, TX, USA

3. University of South Florida, Tampa, FL, USA

4. Brigham Young University, Provo, UT, USA

Funder

Office of Naval Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610977.3634963

Reference50 articles.

1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2. Aneseh Alvanpour, Sumit Kumar Das, Christopher Kevin Robinson, Olfa Nasraoui, and Dan Popa. 2020. Robot Failure Mode Prediction with Explainable Machine Learning. In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 61--66.

3. Gordon Michael Briggs and Matthias Scheutz. 2015. -Sorry, I Can't Do That": Developing Mechanisms to Appropriately Reject Directives in Human-Robot Interactions. In 2015 AAAI Fall Symposium Series.

4. A survey on XAI and natural language explanations

5. Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking

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