Reasoning about knowledge and probability
Journal of the ACM (JACM)
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Logical Structures for Representation of Knowledge and Uncertainty
Logical Structures for Representation of Knowledge and Uncertainty
Reasoning about Uncertainty
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This paper proposes a hierarchy of logics of agents relative to levels of their conflicts, self-conflicts and irrationality to provide a base for several studies on foundations of the theories of uncertainties. These studies include the foundation of known uncertainty theories (probability theory, fuzzy logic, and others) as well as new logics and types of uncertainties. Probability theory, fuzzy logic, and others theories of uncertainties provide a calculus for manipulating with probabilities, membership functions, and other types of uncertainty indicators. However, these theories lack a mechanism for getting initial (basic) uncertainties. The proposed hierarchy of conflicting and irrational agents creates a base for generating uncertainty values, logic operations with these values and for comparing different types of uncertainty for the preference relation. A core concept of this hierarchy is the concept of expansion by superposition that includes fusion and adjustment of contradictory events and statements.