Agents in neural uncertainty

Abstract

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.