Indirect sensing through abstractive learning

Article type: Research Article

Authors: Thornton, Chris^{; *}

Affiliations: Cognitive and Computing Sciences, University of Sussex, Brighton, BN1 9QH, UK

Correspondence: [*] Tel.: +44 1273 678856; E-mail: Christopher.Thornton@firenet.uk.com

Abstract: The paper discusses disparity issues in sensing tasks involving the production of a ‘high-level’ signal from ‘low-level’ signal sources. It introduces an abstraction theory which helps to explain the nature of the problem and point the way to a solution. It proposes a solution based on the use of supervised adaptive methods drawn from artificial intelligence. Finally, it describes a set of empirical experiments which were carried out to evaluate the efficacy of the method.

Keywords: abstraction, sensing, learning, prediction

DOI: 10.3233/IDA-2003-7306

Journal: Intelligent Data Analysis, vol. 7, no. 3, pp. 255-266, 2003