Publication

Handling Unforeseen Failures Using Argumentation-Based Learning

Ayoobi, H., Cao, M., Verbrugge, L. & Verheij, B., 16-May-2019, (Accepted/In press) International Conference on Automation Science and Engineering (CASE) 2019. IEEE, p. 1-8 8 p.

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  • H.Ayoobi et. al. Handling Unforeseen Failures Using Argumentation-Based Learning

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General Purpose Service Robots operate in different environments of a dynamic nature. Even the robot's programmer cannot predict what kind of failure conditions a robot may confront in its lifetime. Therefore, general purpose service robots need to efficiently handle unforeseen failure conditions. This
requires the capability of handling unforeseen failures while the robot is performing a task. Existing research typically offers special-purpose solutions depending on what has been
foreseen at the design time. In this research, we propose a general purpose argumentation-based architecture which is able to autonomously recover
from unforeseen failures. We compare the proposed method with existing incremental online learning methods in the literature. The results show that the proposed argumentation-based learning approach is capable of learning complex scenarios with higher precision than other methods.
Original languageEnglish
Title of host publicationInternational Conference on Automation Science and Engineering (CASE) 2019
PublisherIEEE
Pages1-8
Number of pages8
Publication statusAccepted/In press - 16-May-2019
EventCASE 2019 - International Conference on Automation Science and Engineering - At University of British Columbia, Vancouver, BC, Canada, Vancouver, BC, Canada
Duration: 22-Aug-201926-Aug-2019

Conference

ConferenceCASE 2019 - International Conference on Automation Science and Engineering
CountryCanada
CityVancouver, BC
Period22/08/201926/08/2019

Event

CASE 2019 - International Conference on Automation Science and Engineering

22/08/201926/08/2019

Vancouver, BC, Canada

Event: Conference

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