@inbook{b90c87fb84aa46f0ad787dc2a02556ab,
title = "Application of concept grounding techniques to reduce dimensionality in sensory-motor spaces",
abstract = "Robotic systems are characterised by large state and action spaces, which are problematic for learning and adapting techniques. This paper presents a method to alleviate learning dimensionality problems, by classifying similar states and actions into abstract concepts. Unlike previous techniques, concepts are generated based on the robot-environment interaction rather than being arbitrarily imposed. The validity of this method is demonstrated empirically using a real robot platform in a balltargeting task.",
author = "Pejman Iravani and Johnson, {J H} and L Rapanotti",
year = "2004",
language = "English",
isbn = "978-1-58603-451-1",
volume = "109",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
editor = "E Onaindia and S Staab",
booktitle = "Proceedings of the Second Starting AI Researchers{\textquoteright} Symposium (STAIRS 2004)",
address = "Netherlands",
note = "Proceedings of the Second Starting AI Researchers Symposium Volume 109 Frontiers in Artificial Intelligence and Applications ; Conference date: 01-01-2004",
}