Rough Set adaptive in the Model Based of Cellular Automata and Multi-Agents

The need for intelligent systems has grown in the past decade because of the increasing demand on humans and machines for better performance. The researchers of AI have responded to these needs with the development of intelligent hybrid systems. This paper describes the modeling language for interacting hybrid systems in which we will build a new hybrid model of cellular automata, multiagent technology and rough set theory. Therefore, in our approach, cellular automata form a useful framework for the muliagent simulation model response it in simulated cars in traffic system which lies in adapting the local behavior of individual agent using rough sets to provide an appropriate system-level behavior in grid of interacting organisms. 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 an emergent to the model