Applied Intelligent Systems

Humans have always been hopeless at predicting the futuremost people now generally agree that the margin of viability in prophecy appears to be 1 ten years. Even sophisticated research endeavours in this arena tend to go 2 off the rails after a decade or so. The computer industry has been particularly prone to bold (and often way off the mark) predictions, for example: 'I think there is a world market for maybe five computers' Thomas J. Watson, IBM Chairman (1943), '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), 'There is no reason why anyone would want a computer in their home' Ken Olsen, founder of DEC (1977) and '640K ought to be enough for anybody' Bill Gates, CEO Microsoft (1981). 3 The field of Artificial Intelligence 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 4 over the decades, and a lot of it deservedly so. How often have we groaned in despair at the latest 'by the year-20xx, we will all have(insert your own particular 'hobby horse' here e. g.

Provides practical examples of successful applications of Intelligent Systems to real world problems Includes supplementary material: sn.pub/extras

Klappentext

This carefully edited book presents examples of the successful application of Intelligent Systems techniques to practical problems. The invited contributions, written by international experts in their respective fields, clearly demonstrate what can be achieved when AI systems are used to solve real-world problems. The book covers the field of applied intelligent systems with a broad and deep selection of topics, such as object recognition, robotics, satellite weather prediction, or economics with an industrial focus. This book will be of interest to researchers interested in applied intelligent systems/AI, as well as to engineers and programmers in industry.

Inhalt
1 Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View.- 1.1 'Technical Analysis' in Finance: a Brief Background.- 1.2 The 'Moving Windows' Paradigm.- 1.3 Post-Hoc Performance Assessment.- 1.4 Genetic programming.- 1.5 Support-Vector Machines.- 1.6 Neural Networks.- 1.7 Discussion.- References.- 2 Higher Order Neural Networks for Satellite Weather Prediction.- 2.1 Introduction.- 2.2 Higher Order Neural Networks.- 2.3 Artificial Neural Network Groups.- 2.4 Weather Forecasting & ANNs.- 2.5 HONN Models for Half-hour Rainfall Prediction.- 2.6 ANSER System for Rainfall Estimation.- 2.7 Summary.- 3 Independent Component Analysis.- 3.1 Introduction.- 3.2 Independent Component Analysis Methods.- 3.3 Applications of ICA.- 3.4 Open Problems for ICA Research.- 3.5 Summary.- References.- Appendix Selected ICA Resources.- 4 Regulatory Applications of Artificial Intelligence.- 4.1 Introduction.- 4.2 Solution Spaces, Data and Mining.- 4.3 Artificial Intelligence in Context.- 4.4 Anomaly Detection: ANNs for Prediction/Classification.- 4.5 Formulating Expert Systems to Identify Common Events of Interest.- A Note on the Software.- Acknowledgements.- References.- 5 An Introduction to Collective Intelligence.- 5.1 Collective Intelligence.- 5.2 The Power of Collective Action.- 5.3 Optimisation.- 5.4 Ant Colony Optimisation.- 5.5 Particle Swarm Optimisation.- References.- 6 Where are all the Mobile Robots?.- 6.1 Introduction.- 6.2 Commercial Applications.- 6.3 Research Directions.- 6.4 Conclusion.- A Note on the Figures.- References.- 7 Building Intelligent Legal Decision Support Systems: Past Practice and Future Challenges.- 7.1 Introduction.- 7.2 Jurisprudential Principles for Developing Intelligent Legal Knowledge-Based Systems.- 7.3Early Legal Decision Support Systems.- 7.4 Legal Decision Support on the World Wide Web.- 7.5 Conclusion.- Acknowledgements.- References.- 8 Forming Human-Agent Teams within Hostile Environments.- 8.1 Introduction.- 8.2 Background.- 8.3 Cognitive Engineering.- 8.4 Research Challenge.- 8.5 The Research Environment.- 8.6 The Research Application.- 8.7 Demonstration System.- 8.8 Conclusions.- Acknowledgements.- References.- 9 Fuzzy Multivariate Auto-Regression Method and its Application.- 9.1 Introduction.- 9.2 Fuzzy Data Analysis.- 9.3 Fuzzy Multivariate Auto-Regression Algorithm.- 9.4 Experimental Results.- 9.5 Conclusions.- References.- 10 Selective Attention Adaptive Resonance theory and Object Recognition.- 10.1 Introduction.- 10.2 Adaptive Resonance Theory (ART).- 10.3 Selective Attention Adaptive Resonance Theory.- 10.4 Conclusions.- References.