Agent-based uncertainty logic network

Abstract

Boolean and discrete networks play an important role in many domains such as cellular automata. This paper generalizes that concept of Boolean networks for complex situations with multiple agents acting under uncertainty. This paper creates a logic network 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. An AUT network extend the traditional inferential process by using a set of logic matrices obtained from AUT logic evaluation samples connected in a network. This network computes transformations of AUT logic vectors and gives logic rules for uncertainty situation. The AUT logic network is a generalization of the Boolean network. A Boolean network consists of a set of Boolean variables whose states are determined by other variables in the network An AUT logic network consists of a set of agents presented as vector variables whose states or logic vector evaluations are determined by other variables in the network.