Overview
- Presents new insights into the theories of bounded rationality and counterfactuals
- Explains the theories and applications of artificial intelligence in biomedical sciences, engineering, economics and social sciences
- Proposes linkages between counterfactuals, rationality, causality, correlation and artificial intelligence
- Includes supplementary material: sn.pub/extras
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Reviews
“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)
Authors and Affiliations
Bibliographic Information
Book Title: Artificial Intelligence Techniques for Rational Decision Making
Authors: Tshilidzi Marwala
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-3-319-11424-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-11423-1Published: 03 November 2014
Softcover ISBN: 978-3-319-38298-2Published: 22 September 2016
eBook ISBN: 978-3-319-11424-8Published: 20 October 2014
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XV, 168
Number of Illustrations: 35 b/w illustrations
Topics: Artificial Intelligence, Computational Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Operations Research/Decision Theory, Computer Imaging, Vision, Pattern Recognition and Graphics