Machine Intelligence Volume 13

Preface

 The founder of modern computational logic, J.A. Robinson, opens this
 volume with a chapter on the fields' great forefathers John von
 Neumann and Alan Turing. Stephen Muggleton follows with an analysis of
 Turing's legacy in logic and machine learning, conceiving these not in
 generality, but as specific means of imparting knowledge to computers,
 a theme first articulated by Turing in the late 1940's.

 The present volume records the Machine Intelligence Workshop of 1992,
 held at Strathclyde University's Ross Priory retreat on Loch Lomond,
 Scotland. Here the series entered not only its second quarter-century
 but a new phase. As can be seen in these pages, machine learning
 emerged to declare itself as a seed-bed of new theory, as a practical
 tool in engineering disciplines, and as material for new metal models
 in the human sciences.

 Connections with behavioural and cognitive psychology are illuminated
 in Chapters 9 and 10. The pioneers always stressed these connections.
 In 1953 Claude Shannon had this to say:

 "The problem of how the brain works and how machines may be designed
 to simulate its activity is surely one of the most important and
 difficult facing science... Can we organise machines into a hierarchy
 of levels, as the brain appears to be organised, with the learning of
 the machine gradually progressing up the hierarchy?... How can computer
 memory be organised to learn and remember by association, in a manner
 similar to the human brain?"

 Approaches to learning by association "in a manner similar to the
 human brain" have recently engendered unprecedented interest, one
 might almost say turbulence. Chapter 13 pre-views a joint European
 endeavour of six academic and six industrial laboratories to steer the
 topic towards clearer waters. The complete comparative study is now
 available as a book from Ellis Horwood (Simon and Schuster).

 January 1994                          Stephen Muggleton
                                         Executive Editor
                                       Donald Michie
                                       Koichi Furukawa
                                         Associate Editors

Acknowledgements

 New beginnings par excellence also spring from an agreement concluded
 in 1991 between the Turing Institute, UK and the Japan Society for
 Artificial Intelligence, Tokyo, under the generous auspices of the
 Daiwa Anglo-Japanese Foundation. The Foundation provided funding,
 covering Workshops 13 and 14, to defray travel and attendance costs
 for six Japanese and six British scientists nominated by the
 respective parties. To this we owe the circumstance that this volume
 has been able properly to reflect something of the vigour with which
 the subject is being advanced in Japan.

 We are also indebted to the Royal Society of London for facilitating
 Professor Enn Tyugu's participation from the Estonian Academy of
 Sciences. Strathclyde University, the Turing Institute, and Scottish
 Enterprise also contributed help and resource in the many small ways
 that go towards the making of a great occasion.

 The Editors would also like to express their thanks to Ashwin
 Srinivasan for the many hours of effort involved in persuading LaTeX
 to produce the standard Machine Intelligence look-and-feel within this
 volume. Thanks are also due to the Oxford University Computing
 Laboratory for kindly allowing use of printing and document
 preparation facilities in the production of this volume.

Contents

 PREFACE by Stephen Muggleton, Donald Michie, and Koichi Furukawa

 ACKNOWLEDGEMENTS


 HISTORICAL PERSPECTIVES

 1. Logic Computers, Turing, and von Neumann                          1
    J.A. ROBINSON
 2. Logic and learning: Turing's legacy                              37
    S. MUGGLETON


 INDUCTIVE INFERENCE

 3. A generalization of the least general generalization             59
    H. ARIMURA, T. SHINOHARA, S. OTSUKI & H. ISHIZAKA
 4. The justification of logical theories based on data compression  87
    A. SRINIVASAN, S. MUGGLETON, AND M. BAIN
 5. Utilizing structure information in concept formation            123
    K. HANDA, M. NISHIKIMI AND H. MATSUBARA
 6. The discovery of propositions in noisy data                     143
    HIROSHI TSUKIMOTO AND CHIE MORITA
 7. Learning non-deterministic finite automata from queries         169
    and counterexamples
    T. YOKOMORI


 SCIENTIFIC DOMAINS

 8. Machine Learning and biomolecular modelling                     193
    M.J.E. STERNBERG, R.A. LEWIS, R.D. KING AND S. MUGGLETON
 9. More than meets the eye: animal learning and knowledge induction
                                                                    213
    E.J. KEHOE
 10.Regulation of human cognition and its growth                    247
    C. TREVARTHEN
 11.Large heterogeneous knowledge bases                             269
    E. TYUGU


 EXPERIMENTAL MACHINE LEARNING

 12. Learning optimal chess strategies                              291
    M. BAIN AND S. MUGGLETON
 13.A Comparative study of classification algorithms: Statistical,  311
    Machine Learning and Neural Network
    R.D. KING, R. HENERY, C. FENG AND A. SUTHERLAND


 LEARNING CONTROL

 14. Recent progress with BOXES                                     363
     C. SAMMUT
 15. Building symbolic representations of intuitive real-time       385
     skills from performance data
     D. MICHIE AND R. CAMACHO
 16. Learning perceptually chunked macro operators                  419
     M. SUWA AND H. MOTODA
 17. Inductively speeding up logic programs                         459
     M. NUMAO, T. MARUOKA, AND M. SHIMURA

Bibliographic details

Machine Intelligence 13 - Machine Intelligence and Inductive Learning
Editors:

K. Furukawa

Keio University, Tokyo

Donald Michie

Turing Institute, Glasgow

S. Muggleton

Oxford University Computing Laboratory

Publisher: Clarendon Press 1994

Proceedings of the Thirteenth Machine Intelligence Workshop, held at Strathclyde University, 1992.

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