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Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon's bounded rationality theory are flexible due to advanced signal processing techniques, Moore's Law and artificial intelligence.
Artificial Intelligence Techniques for Rational Decision Making examines and defines the concepts of causal and correlation machines and applies the transmission theory of causality as a defining factor that distinguishes causality from correlation. It develops the theory of rational counterfactuals which are defined as counterfactuals that are intended to maximize theattainment of a particular goal within the context of a bounded rational decision making process. Furthermore, it studies four methods for dealing with irrelevant information in decision making:
- Theory of the marginalization of irrelevant information
- Principal component analysis
- Independent component analysis
- Automatic relevance determination method
In addition it studies the concept of group decision making and various ways of effecting group decision making within the context of artificial intelligence.
Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.
List of contents
Introduction to Rational Decision Making.- Casual Function for Rational Decision Making: Application to Militarized Interstate Disputes.- Correlation Function for Rational Decision Making: Application to Epileptic Activity.- Missing Data Approaches for Rational Decision Making: Application to Anecdotal Data.- Rational Counterfactuals and Decision Making: Application to Interstate Conflict.- Flexibility-Bounded Rationality in Interstate Conflict.- Filtering Irrelevant Information for Rational Decision Making.- Group Decision Making.- Conclusion.- Appendix A: Fourier Transform, Wavelet Transform, Modal Properties and Pseudo-Modal Energies.- Appendix B: Committee of Networks.
Summary
Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon’s bounded rationality theory are flexible due to advanced signal processing techniques, Moore’s Law and artificial intelligence.
Artificial Intelligence Techniques for Rational Decision Making examines anddefines the concepts of causal and correlation machines and applies the transmission theory of causality as a defining factor that distinguishes causality from correlation. It develops the theory of rational counterfactuals which are defined as counterfactuals that are intended to maximize theattainment of a particular goal within the context of a bounded rational decision making process. Furthermore, it studies four methods for dealing with irrelevant information in decision making:
- Theory of the marginalization of irrelevant information
- Principal component analysis
- Independent component analysis
- Automatic relevance determination method
In addition it studies the concept of group decision making and various ways of effecting group decision making within the context of artificial intelligence.
Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.
Additional text
“Each chapter introduces a topic, discusses the
theory for implementing it, and then describes a use case and the results of
its application. … This is an important book. … Copious references, several
pages per chapter, provide voluminous background material for the curious
reader.” (G. R. Mayforth, Computing Reviews, December, 2015)
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"Each chapter introduces a topic, discusses the theory for implementing it, and then describes a use case and the results of its application. ... This is an important book. ... Copious references, several pages per chapter, provide voluminous background material for the curious reader." (G. R. Mayforth, Computing Reviews, December, 2015)
