ECAI 2004 Conference Paper

[full paper]

Combining Multiple Answers for Learning Mathematical Structures from Visual Observation

Paulo Santos, Derek Magee, Anthony Cohn

Learning general truths from the visual observation of simple domains and, further, learning how to use this knowledge are essential capabilities for any intelligent agent to understand and execute informed actions in the real world. The aim of this work is the investigation of the automatic learning of mathematical structures from visual observation. This research was conducted upon a system that combines computer vision with inductive logic programming that was first designed to learn protocol behaviour from observation. In this paper we show how axioms for order and equivalence could be induced from the noisy data provided by a vision system. Noise in the data accounts for the generation of a large amount of possible generalisations by the ILP system, most of which do not represent interesting concepts about the observed domain. In order to automatically choose the best answers among those generated by induction, we propose a method for combining the results of multiple ILP processes by ranking the most interesting answers.

Keywords: Common sense reasoning, Cognitive Vision, Inductive Logic Programming

Citation: Paulo Santos, Derek Magee, Anthony Cohn: Combining Multiple Answers for Learning Mathematical Structures from Visual Observation. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.544-548.