Handling Unforeseen Failures Using Argumentation-Based Learning

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 faster with a lower number of observations. Moreover, the final precision of the proposed method is higher than other methods.