Handling unforeseen failures using argumentation-based learning

Hamed Ayoobi, Ming Cao, Laurina Verbrugge, Bart Verheij

Abstract:

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.

External IDs:doi:10.1109/coase.2019.8843207