ADAPTIVE BEHAVIOR IN CELLULAR AUTOMATA USING ROUGH SET THEORY

摘要

The paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and the response to it in simulated cars. Recently, the examination and modeling of vehicular traffic has become an important subject of research. We propose in this paper, a road-traffic system based on two-dimensional cellular automata combined with rough set theory, to model the flow and jamming that is common in an urban environment. The modeled development process in this paper involves simulated processes of evolution, learning, and self-organization. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of the state machine behavior, which can give emergence to the model.