Applied intelligent systems : new directions
著者
書誌事項
Applied intelligent systems : new directions
（Studies in fuzziness and soft computing, v. 153）
Springer, c2004
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Humans have always been hopeless at predicting the future...most 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.
目次
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.- 1.3.1 The Effect of Dividends.- 1.3.2 Transaction Costs Approximations.- 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.- 2.2.1 Polynomial Higher-Order Neural Networks.- 2.2.2 Trigonometric Higher-Order Neural Networks.- Output Neurons in THONN Model#1.- Second Hidden Layer Neurons in THONN Model#1.- First Hidden Layer Neurons in THONN Model#1.- 2.2.3 Neuron-Adaptive Higher-Order Neural Network.- 2.3 Artificial Neural Network Groups.- 2.3.1 ANN Groups.- 2.3.2 PHONN, THONN & NAHONN Groups.- 2.4 Weather Forecasting & ANNs.- 2.5 HONN Models for Half-hour Rainfall Prediction.- 2.5.1 PT-HONN Model.- 2.5.2 A-PHONN Model.- 2.5.3 M-PHONN Model.- 2.5.4 Satellite Rainfall Estimation Results.- 2.6 ANSER System for Rainfall Estimation.- 2.6.1 ANSER Architecture.- 2.6.2 ANSER Operation.- 2.6.3 Reasoning Network Based on ANN Groups.- 2.6.4 Rainfall Estimation Results.- 2.7 Summary.- Acknowledgements.- References.- Appendix-A Second Hidden Layer (multiply) Neurons.- Appendix-B First Hidden Layer Neurons.- 3 Independent Component Analysis.- 3.1 Introduction.- 3.2 Independent Component Analysis Methods.- 3.2.1 Basic Principles and Background.- 3.2.2 Mutual Information Methods.- 3.2.3 InfoMax ICA Algorithm.- 3.2.4 Natural/Relative Gradient Methods.- 3.2.5 Extended InfoMax.- 3.2.6 Adaptive Mutual Information.- 3.2.7 Fixed Point ICA Algorithm.- 3.2.8 Decorrelation and Rotation Methods.- 3.2.9 Comon Decorrelation and Rotation Algorithm.- 3.2.10 Temporal Decorrelation Methods.- 3.2.11 Molgedey and Schuster Temporal Correlation Algorithm.- 3.2.12 Spatio-temporal ICA Methods.- 3.2.13 Cumulant Tensor Methods.- 3.2.14 Nonlinear Decorrelation Methods.- 3.3 Applications of ICA.- 3.3.1 Guidelines for Applications of ICA.- 3.3.2 Biomedical Signal Processing.- 3.3.3 Extracting Speech from Noise.- 3.3.4 Unsupervised Classification Using ICA.- 3.3.5 Computational Finance.- 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.- 4.4.1 Training to Classify on Spare Data Sets.- 4.4.2 Training to Predict on Dense Data Sets.- 4.4.3 Feature Selection for and Performance of Anomaly Detection Suites.- 4.4.4 Interpreting Anomalies.- 4.4.5 Other Approaches to Anomaly Detection.- 4.4.6 Variations of BackProp' ANNs for Use with Complex Data Sets.- 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.- 5.1.1 A Simple Example of Stigmergy at Work.- 5.2 The Power of Collective Action.- 5.3 Optimisation.- 5.3.1 Optimisation in General.- 5.3.2 Shades of Optimisation.- 5.3.3 Exploitation versus Exploration.- 5.3.4 Example of Common Optimisation Problems.- Minimum Path Length.- Function Optimisation.- Sorting.- Multi-Component Optimisation.- 5.4 Ant Colony Optimisation.- 5.4.1 Ant Systems - the Basic Algorithm.- The Problem with AS.- 5.4.2 Ant Colony Systems.- 5.4.3 Ant Multi-Tour System (AMTS).- 5.4.4 Limiting the Pheromone Density - the Max-Min Ant System.- 5.4.5 An Example: Using Ants to Solve a (simple) TSP.- 5.4.6 Practical Considerations.- 5.4.7 Adding a Local Heuristic.- 5.4.8 Other Uses for ACO.- 5.4.9 Using Ants to Sort.- An Example of Sorting Using ACO.- 5.5 Particle Swarm Optimisation.- 5.5.1 The Basic Particle Swarm Optimisation Algorithm.- 5.5.2 Limitations of the Basic Algorithm.- 5.5.3 Modifications to the Basic PSO Algorithm.- Choosing the Position S.- The Problem of a finite t.- Aggressively Searching Swarms.- Adding Memory to Each Particle.- 5.5.4 Performance.- 5.5.5 Solving TSP Problems Using PSO.- PSO Performance on a TSP.- 5.5.6 Practical Considerations.- 5.5.7 Scalability and Adaptability.- References.- 6 Where are all the Mobile Robots?.- 6.1 Introduction.- 6.2 Commercial Applications.- 6.2.1 Robot Couriers.- 6.2.2 Robot Vacuum Cleaners.- 6.2.3 Robot Lawn Mowers.- 6.2.4 Robot Pool Cleaners.- 6.2.5 Robot People Transporter.- 6.2.6 Robot Toys.- 6.2.7 Other Applications.- 6.2.8 Getting a Robot to Market.- 6.2.9 Wheeled Mobile Robot Research.- 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.- 7.1.1 Benefits of Legal Decision Support Systems to the Legal Profession.- 7.1.2 Current Research in AI and Law.- 7.2 Jurisprudential Principles for Developing Intelligent Legal Knowledge-Based Systems.- 7.2.1 Reasoning with Open Texture.- 7.2.2 The Inadequacies of Modelling Law as a Series of Rules.- 7.2.3 Landmark and Commonplace Cases.- 7.3 Early Legal Decision Support Systems.- 7.3.1 Rule-Based Reasoning.- 7.3.2 Case-Based Reasoning and Hybrid Systems.- 7.3.3 Knowledge Discovery in Legal Databases.- 7.3.4 Evaluation of Legal Knowledge-Based Systems.- 7.3.5 Explanation and Argumentation in Legal Knowledge-Based Systems.- 7.4 Legal Decision Support on the World Wide Web.- 7.4.1 Legal Knowledge on the WWW.- 7.4.2 Legal Ontologies.- 7.4.3 Negotiation Support Systems.- 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.- 8.4.1 Human-Agent Teaming.- 8.4.2 Agent Learning.- 8.5 The Research Environment.- 8.5.1 The Concept of Situational Awareness.- 8.5.2 The Unreal Tournament Game Platform.- 8.5.3 The Jack Agent.- 8.6 The Research Application.- 8.6.1 The Human Agent Team.- 8.6.2 The Simulated World Within Unreal Tournament.- 8.6.3 Interacting With Unreal Tournament.- 8.6.4 The Java Extension.- 8.6.5 The Jack Component.- 8.7 Demonstration System.- 8.7.1 Wrapping Behaviours in Capabilities.- 8.7.2 The Exploring Behaviours.- 8.7.3 The Defending Behaviour.- 8.8 Conclusions.- Acknowledgements.- References.- 9 Fuzzy Multivariate Auto-Regression Method and its Application.- 9.1 Introduction.- 9.2 Fuzzy Data Analysis.- 9.2.1 Fuzzy Regression.- 9.2.2 Fuzzy Time Series Analysis.- 9.2.3 Fuzzy Linear Regression (FLR).- Basic Definitions.- Linear Programming Problem.- 9.3 Fuzzy Multivariate Auto-Regression Algorithm.- Example - Gas Furnace Data Processed by MAR.- 9.3.1 Model Selection.- 9.3.2 Motivation for FLR in Fuzzy MAR.- 9.3.3 Fuzzification of Multivariate Auto-Regression.- 9.3.4 Bayesian Information Criterion in Fuzzy MAR.- 9.3.5 Obtaining a Linear Function for a Variable.- 9.3.6 Processing of Multivariate Data.- 9.4 Experimental Results.- 9.4.1 Experiments with Gas Furnace Data.- 9.4.2 Experiments with Interest Rate Data.- 9.4.3 Discussion of Experimental Results.- 9.5 Conclusions.- References.- 10 Selective Attention Adaptive Resonance theory and Object Recognition.- 10.1 Introduction.- 10.2 Adaptive Resonance Theory (ART).- 10.2.1 Limitations of ART's Attentional Subsystem with Cluttered Inputs.- 10.3 Selective Attention Adaptive Resonance Theory.- 10.3.1 Neural Network Implementation of SAART.- Postsynaptic Cellular Activity.- Excitatory Postsynaptic Potential.- Lateral Competition.- Transmitter Dynamics.- 10.3.2 Translation-invariant 2D Shape Recognition.- 10.4 Conclusions.- References.
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