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

Authors:
Vesna Poprcova;Georgi Stojanov;Andrea Kulakov
Affiliations:
Ss. Cyril and Methodius University of Skopje, Macedonia;The American University of Paris, France;Ss. Cyril and Methodius University of Skopje, Macedonia
Venue:
International Journal of Agent Technologies and Systems
Year:
2010

Citing 3
Cited 0

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Inductive Logic Programming and Embodied Agents: Possibilities and Limitations

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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.