Inductive Logic Programming (ILP) and Reasoning by Analogy in Context of Embodied Robot Learning

Abstract

The ability to reason by analogy is essential for many cognitive processes from low-level and high-level perception to categorization. Intuitively, the idea is to use what is already known to explain new observations that appear similar to old knowledge. In a sense, it is opposite of induction, where to explain the observations one comes up with new hypotheses/theories. Therefore, a system capable of both types of reasoning would be superior. In this paper, the authors present an overview of Inductive Logic Programming (ILP) systems that use reasoning by analogy and discuss the results of combining Analogical Prediction with an ILP system, showing that, for some cases, it is possible to improve significantly the learning speed of the ILP system. This paper will examine the problems that arise in the context of a physically embodied robot that tries to learn regularities in its environment.

Towards iterative learning of autonomous robots using ILP

“…We exploit the knowledge of sensor accuracy to define BK that is able to handle the noise. ILP learners are also not good in handling large amount of data traces [10]. [3] shows that using large data traces significantly increases the time complexity of learning problems.…”