| We constantly make decisions which are simply "good enough" rather than optimal--a type of decision for which Wynn Stirling has adopted the word "satisficing". Most computer decision making algorithms, however, seek only the optimal solution based on rigid criteria and reject others. Outlining an alternative approach, this book uses novel algorithms and techniques to more closely model the way humans make decisions. It is, therefore, of interest to engineers, computer scientists and mathematicians working on artificial intelligence and expert systems.
In our day-to-day lives we constantly make decisions which are simply 'good enough' rather than optimal. Most computer-based decision making algorithms, on the other hand, doggedly seek only the optimal solution based on rigid criteria and reject any others. In this book, Professor Stirling outlines an alternative approach, using novel algorithms and techniques which can be used to find satisficing solutions. Building on traditional decision and game theory, these techniques allow decision-making systems to cope with more subtle situations where self and group interests conflict, perfect solutions can't be found and human issues need to be taken into account - in short, more closely modelling the way humans make decisions. The book will therefore be of great interest to engineers, computer scientists and mathematicians working on artificial intelligence and expert systems.
Text outlines an approach, using novel algorithms and techniques, which can be used to find satisficing solutions: good enough rather than optimal. Builds on traditional decision and game theory. Of interest to engineers, computer scientists, and mathematicians working on artificial intelligence and expert systems.
About the Author
Wynn Stirling is a Professor of Electrical and Computer Engineering at Brigham Young University, where he teaches stochastic processes, control theory and signal processing. |
