TY - GEN
T1 - Combining multiple answers for learning mathematical structures from visual observation
AU - Santos, Paulo
AU - Magee, Derek
AU - Cohn, Anthony
AU - Hogg, David
PY - 2004
Y1 - 2004
N2 - Learning general truths from the 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 transitivity, reflexivity and symmetry axioms could be induced from the noisy data provided by the vision system. Noise in the data accounts for the generation of a large number 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.
AB - Learning general truths from the 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 transitivity, reflexivity and symmetry axioms could be induced from the noisy data provided by the vision system. Noise in the data accounts for the generation of a large number 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.
UR - http://www.scopus.com/inward/record.url?scp=85017402471&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85017402471
SN - 978-1-58603-452-8
T3 - Frontiers in Artificial Intelligence and Applications
SP - 544
EP - 548
BT - ECAI 2004
A2 - de Mantaras, Ramon Lopez
A2 - Saitta, Lorenza
PB - IOS PRESS
T2 - 16th European Conference on Artificial Intelligence, ECAI 2004
Y2 - 22 August 2004 through 27 August 2004
ER -