Data preparation for inductive learning in robotics

Abstract

The application of logic-based learning algorithms in real-world domains, such as robotics, requires extensive data engineering, including the transformation of numerical tabular representations of real-world data to logic-based representations, feature and concept selection, the generation of the respective descriptions, and the composition of training and test sets, which meet the requirements of the respective learning algorithms. We are developing a tool, which supports a user of inductive logic-based algorithms with handling these tasks. The tool is developed in the context of a robot navigation domain, in which different logic-based algorithms are applied to learn operational concepts. The paper is written in English.