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Becoming incrementally reactive: on-line learning of an evolving decision tree array for robot navigation

Published online by Cambridge University Press:  01 May 1999

G.H. Shah Hamzei ,
D.J. Mulvaney  and
I. Sillitoe
G.H. Shah Hamzei
Affiliation:
Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leics LE11 3TU (UK). E-mail: [email protected]
D.J. Mulvaney
Affiliation:
Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leics LE11 3TU (UK). E-mail: [email protected]
I. Sillitoe
Affiliation:
Department of Technology, University of Borås, 501 15 Borås (Sweden). E-mail: [email protected]
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Abstract

This paper proposes a novel hierarchical multi-layer decision tree for representing reactive robot navigation knowledge. In this representation, the perception space is decomposed into a hierarchical set of worlds reflecting environments which are homogeneous in nature and which vary in complexity in an ordered manner. Each world is used to produce a corresponding decision tree which is trained incrementally. The instantaneous perception of the robot is used to select an appropriate rule from the decision tree and a sequence of rule activations form the complete trajectory. The ability to keep the knowledge complexity manageable and under control is an important aspect of the technique.

Keywords

Incremental learningHierarchical learningDecision treesMachine learningIntelligent navigationRoboticsIncremental tree induction (ITI)
Type
Research Article
Information
Robotica , Volume 17 , Issue 3 , May 1999 , pp. 325 - 334
Copyright
© 1999 Cambridge University Press

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