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On using constructivism in neural classifier systems

Bull, Larry


Lawrence Bull
School Director (Research & Enterprise) and Professor


Juan J. Merelo

Panagiotis Adamidis

Hans-Georg Beyer


For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn novel behaviours. This paper explores the potential of using constructivism within the neural classifier system architecture as an approach to realise such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. Results are presented which suggest it is possible to allow appropriate internal rule complexity to emerge during learning and that the structure indicates underlying features of the task.


Bull, L. (2002). On using constructivism in neural classifier systems. In J. J. Merelo, P. Adamidis, & H. Beyer (Eds.), In Parallel Problem Solving from Nature — PPSN VII. , (558-567).

Conference Name Parallel Problem Solving from Nature — PPSN VII
Conference Location Granada, Spain
Start Date Sep 7, 2002
End Date Sep 11, 2002
Publication Date Jan 1, 2002
Pages 558-567
Series Title Lecture Notes in Computer Science
Series Number 2439
Series ISSN 0302-9743
Book Title Parallel Problem Solving from Nature — PPSN VII
ISBN 9783540441397
Keywords computation by abstract devices, algorithm analysis and problem complexity, processor architectures, artificial intelligence, programming techniques, evolutionary biology
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