Knowledge representations for learning control

Abstract

Covers such issues as the meaning of representation, the role representation plays in programs for learning control, expressive power of particular representation schemes, and the impact of choosing a particular representation on the complexity of reasoning and learning. The authors cover intelligence-in-the limb versus intelligence; learning efficiency as the critical issue in learning control, selective versus constructive induction and map learning: binary- and fuzzy-logic-base knowledge representation for intelligent robotic systems; and knowledge representation, definability, and self-reference.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>