Reasoning about knowledge and probability
Journal of the ACM (JACM)
Interpretations of various uncertainty theories using models of modal logic: a summary
Fuzzy Sets and Systems
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Canonical forms of fuzzy truthoods by meta-theory based upon modal logic
Information Sciences: an International Journal
Logical Structures for Representation of Knowledge and Uncertainty
Logical Structures for Representation of Knowledge and Uncertainty
Reasoning about Uncertainty
Adaptive Bidding in Single-Sided Auctions under Uncertainty: An Agent-based Approach in Market Engineering (Whitestein Series in Software Agent Technologies and Autonomic Computing)
On the relevance of some families of fuzzy sets
Fuzzy Sets and Systems
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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This paper models neural uncertainty using a concept of the Agent-based Uncertainty Theory (AUT). The AUT is based on complex fusion of crisp (non-fuzzy) conflicting judgments of agents. It provides a uniform representation and an operational empirical interpretation for several uncertainty theories such as rough set theory, fuzzy sets theory, evidence theory, and probability theory. The AUT models conflicting evaluations that are fused in the same evaluation context. This paper shows that the neural fusion at the synapse can be modeled by the AUT. The neuron is modeled as an operator that transforms classical logic expressions into many-valued logic expressions. The new neural network has neurons at two layers. The first-layer agents implement the classical logic operations, but at the second level, neurons or nagents (neuron agents) compute the same logic expression with different results for different agent inputs. The motivation for such neural network is to provide high flexibility and logic adaptation of the neural model.