Applied Intelligent Systems: New Directions

338 Pages·2004·4.341 MB·English

by  David Edelman, Pam Davy (auth.), Professor John Fulcher, Professor Lakhmi C. Jain (eds.)

J. Fulcher, L. C. Jain (Eds.)
Applied Intelligent Systems
Springer-Verlag Berlin Heidelberg GmbH
Studies in Fuzziness and Soft Computing, Volume 153
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John Fulcher
Lakhmi C. Jain (Eds.)
Applied Intelligent
Systems
New Directions
123
Professor John Fulcher Professor Lakhmi C. Jain
University of Wollongong University of South Australia
School of Information Knowledge-Based Intelligent
Technology & Computer Science Engineering Systems Centre
2522 Wollongong, NSW Mawson Lakes
Australia 5095 Adelaide
E-mail: [email protected] Australia
E-mail: [email protected]
ISSN 1434-9922
ISBN 978-3-642-05942-1 ISBN 978-3-540-39972-8 (eBook)
DOI 10.1007/978-3-540-39972-8
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© Springer-Verlag Berlin Heidelberg 2004
Originally published by Springer-Verlag Berlin Heidelberg in 2004
Softcover reprint of the hardcover 1st edition 2004
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Dedicated to
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Preface
Humans have always been hopeless at predicting the future…most people
now generally agree that the margin of viability in prophecy appears to be
ten years.1 Even sophisticated research endeavours in this arena tend to go
off the rails after a decade or so.2 The computer industry has been
particularly prone to bold (and often way off the mark) predictions, for
example:
(cid:1) ‘I think there is a world market for maybe five computers’ Thomas J.
Watson, IBM Chairman (1943),
(cid:1) ‘I have traveled the length and breadth of this country and talked with
the best people, and I can assure you that data processing is a fad that
won’t last out the year’ Prentice Hall Editor (1957),
(cid:1) ‘There is no reason why anyone would want a computer in their home’
Ken Olsen, founder of DEC (1977) and
(cid:1) ‘640K ought to be enough for anybody’ Bill Gates, CEO Microsoft
(1981).
The field of Artificial Intelligence3 – right from its inception – has been
particularly plagued by ‘bold prediction syndrome’, and often by leading
practitioners who should know better. AI has received a lot of bad press
over the decades, and a lot of it deservedly so.4 How often have we
groaned in despair at the latest ‘by the year-20xx, wewill all have…(insert
your own particular ‘hobby horse’ here – e.g. autonomous robot vacuum
cleaners that will absolve us of the need to clean our homes…etc)’ – and
this is to completely ignore the reality that most of the world’s population
1 Davies S (1996) Monitor: Extinguishing Privacy on the Information
Superhighway, Pan Macmillan, Sydney.
2 Naisbitt J (1982) Megatrends: Ten New Directions Transforming Our Lives,
Macdonald, London.
3 whatever that term means (we could present another entire book debating this
topic).
4 Fulcher JA (2001) Practical (Artificial) Intelligence, Invited Keynote Speech, 5th
National Thai Conf Computer Science & Engineering, Chiang Mai, 7-9
November: i23-i28.
VIII Preface
does not have internet access5, let alone own a computer6 or telephone7 –
indeed 1.3 billion do not have access to clean drinking water8. Obviously
the ‘we’ of these predictions refers only to a small minority of first-world
citizens. Don’t these advocates (zealots?) realize the harm that such
misplaced predictions cause in the longer term? Indeed, their misplaced
enthusiasm damages allof us who work in the field.
Decades of spectacular failures and unfulfilled promises can only serve
to antagonize the public and get them offside for years to come. More
specifically, some AI researchers have misjudged the difficulty of
problems and oversold the prospects of short-term progress based on the
initial results.910 To put it another way, the methods that sufficed for
demonstration on one or two simple examples turned out to fail miserably
when tried out on wider selections of problems.11
This then leads us to the motivation for the present book. Your Editors
felt it was timely to take a step back from the precipice, as it were, to pause
and reflect, and rather than indulge in yet more bold predictions, to report
on some intelligent systems that actually work – now (and not at some
mythical time in the future).
With this in mind, we invited the authors herein – all international
experts in their respective fields – to contribute Chapters on intelligent
techniques that have been tried and proved to work on real-world
problems. We should also point out that our use of ‘intelligent’ in this
context reflects the intelligence of the authors who have created these
various techniques, and not on some nebulous ‘ghost in the machine’.
The book commences with Edelman & Davy’s application of Genetic
Programming, Support Vector Machines and Artificial Neural Networks to
financial market predictions, and from their results they draw conclusions
aboutthe weak form of the Efficient Markets Hypothesis.
5 http://unstats.un.org/unsd (ITU estimates – e.g. Iceland 60%; Spain 14%; China
1.74%; India 0.54%; Somalia 0.01% per 100 population)
6 http://unstats.un.org/unsd (ITU estimates – e.g. USA 57%; Slovenia 27.5%;
China 1.59%; India 0.45%; Niger 0.056% per 100 population)
7 http://unstats.un.org/unsd (ITU estimates – e.g. Monaco 147%; Australia 100%;
Estonia 75%; Mexico 27%; China 17.76%; India 3.56%; Afghanistan 0.13%
per 100 population)
8 http://unstats.un.org/unsd (WHO/UNICEF estimates)
9 Allen J (1998) AI Growing Up; the Changes and Opportunities, AI Magazine,
Winter: 13-23.
10 Hearst M and Hirsh H (2000) AI’s Greatest Trends and Controversies, IEEE
Intelligent Systems, January/February:8-11.
11 Russell S and Norvig P (1995) AI: A Modern Approach, Prentice Hall,
Englewood Cliffs, NJ.
Preface IX
Chapter 2 covers the Higher Order Neural Network models developed
by Zhang & Fulcher. HONNs (and HONN groups) have been successfully
applied to human face recognition, financial time series modeling and
prediction, as well as to satellite weather prediction – it is the latter that is
reported on in this volume.
Back demonstrates the power of Independent Component Analysis in
Chapter 3, and cites examples drawn from biomedical signal processing
(ECG), extracting speech from noise, unsupervised classification (non-
invasive oil flow monitoring and banknote fraud detection), and financial
market prediction.
Chapter 4 focuses on the application of AI techniques to regulatory
applications in health informatics. Copland describes an innovative
combination of Evolutionary Algorithms and Artificial Neural Networks
which he uses as his primary Data Mining tool when investigating
servicing by medical doctors.
Swarms and their collective intelligence are the subject of the next
chapter. Hendtlass first describes several ant colony optimization
algorithms, then proceeds to show how they can be applied both to the
Travelling SalesPerson problem and sorting (of the iris data set).
In Chapter 6, McKerrow asks the question: ‘Where have all the mobile
robots gone?’, and in the process restores some sanity to counteract some
bold predictions by practitioners who should know better. The real-world
commercial applications covered in this Chapter include robot couriers,
vacuum cleaners, lawn mowers, pool cleaners, and people transporters.
Zeleznikow’s expertise with intelligent legal decision support systems is
brought to the fore in Chapter 7, and places this emerging field within an
historical context. Rule-based reasoners, case-based and hybrid systems,
Knowledge Discovery in Databases and web-based systems are all
covered.
Chapter 8 is devoted to Human-Agent teams within hostile
environments. Sioutis and his co-authors illustrate their ideas within the
context of the Jack agent shell and interactive 3D games such as Unreal
Tournament.
The Fuzzy Multivariate Auto-Regression method is the focus of Chapter
9. Sisman-Yilmaz and her co-authors show how Fuzzy MAR can be
applied to both Gas Furnace and Interest Rate data.
In Chapter 10, Lozo and his co-authors describe an extension of ART –
Selective Attention Adaptive Resonance Theory – and illustrate its
usefulness when applied to distortion-invariant 2D shape recognition
embeddedin clutter.
We hope these invited Chapters serve two functions: firstly, presentation
of tried and proven ‘intelligent’ techniques, and more especially the
particular application niche(s) in which they have been successfully
X Preface
applied. Secondly, we hope to restore some much needed public
confidence in a field that has become tarnished by bold, unrealistic
predictions for the future.
Lastly, we would like to thank all our contributing authors who so
willingly took time from their busy schedules to produce the quality
Chapters contained herein.
Enjoy your reading.
University ofWollongong John Fulcher
University of South Australia Lakhmi C Jain
Spring 2004
Table of Contents
1 Adaptive Technical Analysis in the Financial Markets Using
Machine Learning: a Statistical View
David Edelman and Pam Davy
1.1 ‘Technical Analysis’ in Finance: a Brief Background 1
1.2 The ‘Moving Windows’ Paradigm 2
1.3 Post-Hoc Performance Assessment 3
1.3.1 The Effectof Dividends 5
1.3.2 Transaction Costs Approximations 6
1.4 Genetic programming 7
1.5 Support-Vector Machines 10
1.6 Neural Networks 12
1.7 Discussion 14
References 15
2 Higher Order NeuralNetworks for Satellite Weather
Prediction
Ming Zhang and John Fulcher
2.1 Introduction 17
2.2 Higher Order Neural Networks 18
2.2.1 Polynomial Higher-Order Neural Networks 20
2.2.2 Trigonometric Higher-Order Neural Networks 23
Output Neurons in THONN Model#1 24
Second Hidden Layer Neurons in THONN Model#1 27
First Hidden Layer Neurons in THONN Model#1 30
2.2.3 Neuron-Adaptive Higher-Order Neural Network 30
2.3 Artificial Neural Network Groups 33
2.3.1 ANN Groups 33
2.3.2 PHONN, THONN & NAHONN Groups 34
2.4 Weather Forecasting & ANNs 35
2.5 HONN Modelsfor Half-hour Rainfall Prediction 36
2.5.1 PT-HONN Model 36
2.5.2 A-PHONN Model 37