Handling Unforeseen Failures Using Argumentation-Based LearningAyoobi, 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.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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.
|Title of host publication||International Conference on Automation Science and Engineering (CASE) 2019|
|Number of pages||8|
|Publication status||Accepted/In press - 16-May-2019|
|Event||CASE 2019 - International Conference on Automation Science and Engineering - At University of British Columbia, Vancouver, BC, Canada, Vancouver, BC, Canada|
Duration: 22-Aug-2019 → 26-Aug-2019
|Conference||CASE 2019 - International Conference on Automation Science and Engineering|
|Period||22/08/2019 → 26/08/2019|
CASE 2019 - International Conference on Automation Science and Engineering
22/08/2019 → 26/08/2019Vancouver, BC, Canada