Multivalued Logic For Decisionmaking Under Uncertainty Computer Science Foundations And Applied Logic 2024th Edition Evgeny Kagan by Evgeny Kagan, Alexander Rybalov, Ronald Yager 9783031747618, 3031747615 instant download after payment.

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.