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Multi-valued Logic for Decision-Making Under Uncertainty (Computer Science Foundations and Applied Logic) - Softcover
Synopsis
Multi-valued and fuzzy logics provide mathematical and computational tools for handling imperfect information and decision-making with rational collective reasoning and irrational individual judgements.
The suggested implementation of multi-valued logics is based on the uninorm and absorbing norm with generating functions defined by probability distributions. Natural extensions of these logics result in non-commutative and non-distributive logics. In addition to Boolean truth values, these logics handle subjective truth and false values and model irrational decisions. Dynamics of decision-making are specified by the subjective Markov process and learning – by neural network with extended Tsetlin neurons. Application of the suggested methods is illustrated by modelling of irrational economic decisions and biased reasoning in the wisdom-of-the-crowd method, and by control of mobile robots and navigation of their groups.
Topics and features:
- Bridges the gap between fuzzy and probability methods
- Includes examples in the field of machine-learning and robots’ control
- Defines formal models of subjective judgements and decision-making
- Presents practical techniques for solving non-probabilistic decision-making problems
- Initiates further research in non-commutative and non-distributive logics
The book forms a basis for theoretical studies and practice of decision-making under uncertainty and will be useful for computer scientists and mathematicians interested in multi-valued and fuzzy logic, as well as for engineers working in the field of data mining and data analysis.
"synopsis" may belong to another edition of this title.
About the Author
Dr. Evgeny Kagan is with the Faculty of Engineering, Ariel University, Israel.
Dr. Alexander Rybalov is with the LAMBDA Laboratory, Tel-Aviv University, Israel.
Prof. Ronald Yager is with the Machine Learning Institute, Yona College, New York, USA.
From the Back Cover
Multi-valued and fuzzy logics provide mathematical and computational tools for handling imperfect information and decision-making with rational collective reasoning and irrational individual judgements.
The suggested implementation of multi-valued logics is based on the uninorm and absorbing norm with generating functions defined by probability distributions. Natural extensions of these logics result in non-commutative and non-distributive logics. In addition to Boolean truth values, these logics handle subjective truth and false values and model irrational decisions. Dynamics of decision-making are specified by the subjective Markov process and learning – by neural network with extended Tsetlin neurons. Application of the suggested methods is illustrated by modelling of irrational economic decisions and biased reasoning in the wisdom-of-the-crowd method, and by control of mobile robots and navigation of their groups.
Topics and features:
- Bridges the gap between fuzzy and probability methods
- Includes examples in the field of machine-learning and robots’ control
- Defines formal models of subjective judgements and decision-making
- Presents practical techniques for solving non-probabilistic decision-making problems
- Initiates further research in non-commutative and non-distributive logics
The book forms a basis for theoretical studies and practice of decision-making under uncertainty and will be useful for computer scientists and mathematicians interested in multi-valued and fuzzy logic, as well as for engineers working in the field of data mining and data analysis.
Dr. Evgeny Kagan is with the Faculty of Engineering, Ariel University, Israel; Dr. Alexander Rybalov is with the LAMBDA Laboratory, Tel-Aviv University, Israel; and Prof. Ronald Yager is with the Machine Learning Institute, Yona College, New York, USA.
"About this title" may belong to another edition of this title.
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Taschenbuch. Condition: Neu. Multi-valued Logic for Decision-Making Under Uncertainty | Evgeny Kagan (u. a.) | Taschenbuch | Computer Science Foundations and Applied Logic | viii | Englisch | 2026 | Birkhäuser | EAN 9783031747649 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 134809060
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Multi-valued and fuzzy logics provide mathematical and computational tools for handling imperfect information and decision-making with rational collective reasoning and irrational individual judgements.The suggested implementation of multi-valued logics is based on the uninorm and absorbing norm with generating functions defined by probability distributions. Natural extensions of these logics result in non-commutative and non-distributive logics. In addition to Boolean truth values, these logics handle subjective truth and false values and model irrational decisions. Dynamics of decision-making are specified by the subjective Markov process and learning - by neural network with extended Tsetlin neurons. Application of the suggested methods is illustrated by modelling of irrational economic decisions and biased reasoning in the wisdom-of-the-crowd method, and by control of mobile robots and navigation of their groups.Topics and features:Bridges the gap between fuzzy and probability methodsIncludes examples in the field of machine-learning and robots' controlDefines formal models of subjective judgements and decision-makingPresents practical techniques for solving non-probabilistic decision-making problemsInitiates further research in non-commutative and non-distributive logicsThe book forms a basis for theoretical studies and practice of decision-making under uncertainty and will be useful for computer scientists and mathematicians interested in multi-valued and fuzzy logic, as well as for engineers working in the field of data mining and data analysis.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 204 pp. Englisch. Seller Inventory # 9783031747649
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Multi-valued and fuzzy logics provide mathematical and computational tools for handling imperfect information and decision-making with rational collective reasoning and irrational individual judgements.The suggested implementation of multi-valued logics is based on the uninorm and absorbing norm with generating functions defined by probability distributions. Natural extensions of these logics result in non-commutative and non-distributive logics. In addition to Boolean truth values, these logics handle subjective truth and false values and model irrational decisions. Dynamics of decision-making are specified by the subjective Markov process and learning - by neural network with extended Tsetlin neurons. Application of the suggested methods is illustrated by modelling of irrational economic decisions and biased reasoning in the wisdom-of-the-crowd method, and by control of mobile robots and navigation of their groups.Topics and features:Bridges the gap between fuzzy and probability methodsIncludes examples in the field of machine-learning and robots' controlDefines formal models of subjective judgements and decision-makingPresents practical techniques for solving non-probabilistic decision-making problemsInitiates further research in non-commutative and non-distributive logicsThe book forms a basis for theoretical studies and practice of decision-making under uncertainty and will be useful for computer scientists and mathematicians interested in multi-valued and fuzzy logic, as well as for engineers working in the field of data mining and data analysis. Seller Inventory # 9783031747649