Subjective logic. A formalism for reasoning under uncertainty
The main characteristic of subjective logic consists in including second-order uncertainty in the form of uncertainty mass; by including the uncertainty dimension, concepts from Bayesian theory and probabilistic logic are redefined. The book is organized as follows: {\parindent=0.7cm \begin{itemize}\item[{\(\bullet\)}] Basic ideas of subjective logic are exposed in the first chapter. \item[{\(\bullet\)}] Fundamental elements and terminology used are presented in Chapter 2. \item[{\(\bullet\)}] Representations and notations for subjective opinions are described in Chapter 3. \item[{\(\bullet\)}] The concepts of sharp belief mass, vague belief mass, focal uncertainty mass and its concatenation as a tuple called mass-sum are defined in Chapter 4; decision making involving these concepts is presented in the second part of this chapter. \item[{\(\bullet\)}] Chapter 5 compares subjective logic with other reasoning frameworks and presents an overview of subjective logic operators. \item[{\(\bullet\)}] The next three chapters describe various operations with subjective opinions: addition, subtraction and complement as a generalization of the corresponding operators for probabilities; binomial multiplication and comultiplication, binomial division and codivision; multinomial multiplication and division, which generalize similar operations for binomial opinions. \item[{\(\bullet\)}] The next two chapters discuss conditional reasoning and the deduction operator in subjective logic and, respectively, subjective Bayes theorem and subjective abduction operator. \item[{\(\bullet\)}] Joint and marginal opinions represent the subject of Chapter 11. \item[{\(\bullet\)}] How the opinions from different sources about the same domains of interest can be combined to obtain a single opinion is analyzed in Chapter 12; the opposite of this principle is presented in the next chapter in the form of two concepts: fission and unfusion. \item[{\(\bullet\)}] The notion of trust opinions in subjective logic is interpreted as reliability trust; this concept is explained in Chapter 14, and how to deal with trust networks is described in Chapter 15. \item[{\(\bullet\)}] The performance and quality of service objects are collected and analyzed by reputation systems consisting of a collection network and a reputation score computation engine; this subject is treated in Chapter 16. \item[{\(\bullet\)}] Bayesian networks are generalized to subjective networks, in the last chapter, in two ways: by using subjective logic instead of probabilistic reasoning and by integrating subjective trust networks. \end{itemize}}
scientific article; zbMATH DE number 6699035