Fusion in agent-based uncertainty theory and neural image of uncertainty

In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation, the neural network is composed of agents or neurons, which work to implement uncertainty calculus and many valued logics from crisp perceptual input. This idea is closely related to the dynamic logic approach and recent cognitive science experimental discoveries. According to this model having crisp perceptual input, brain (1) produces a less certain representation, (2) processes input at this uncertainty level of representation, (3) converts results to the next more certain level of information representation, (4) processes this information and (5) repeats these steps several times until the acceptable level of certainty is reached. To build such model we rely not on the binary logic but on the logic of the uncertainty to obtain the high flexibility and logic adaptation of the described process. This paper presents a concept of the agent-based uncertainty theory (AUT) based on complex fusion of crisp conflicting judgments of agents Communication among agents is modeled by the fusion process in the neural elaboration.