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The aim is identifying and analyzing some well‐defined types of emergence where the paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and response in simulated cars.

This paper proposes, as practical part, a road traffic system based on two‐dimensional cellular automata combined with rough set theory to model the flow and jamming that is suitable to an urban environment.

The automaton mimics realistic traffic rules that apply in everyday experience.

The modeled development process in this paper involves simulated processes of evolution, learning and self‐organization.

Recently, the examination and modeling of vehicular traffic has become an important subject of research.

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.

The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. This paper introduces the emergent computational paradigm and discusses its applicability and potential in rough set theory.

A conceptual discussion and approach are taken.

For accepting a system is displaying an emergent behavior, the system should be constructed by describing local elementary interactions between components in different ways of describing global behavior and properties of the running system over a period of time. The proposals of an emergent computation structure for implementing basic rough sets theory operators are also given in this paper.

The results will have an important impact on the development of new methods for knowledge discovery in databases, in particular for development of algorithmic methods for pattern extraction from data.

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large‐scale systems. In order to solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined Function Genetic Programming (ADF‐GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e‐commerce usage will be used to observe the effectiveness of the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent system was observed.

A methodology for using rough set for preference modeling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on rough set combined with genetic programming. Genetic programming belongs to the most new techniques in applications of artificial intelligence. Rough set theory, which emerged about 20 years back, is nowadays a rapidly developing branch of artificial intelligence and soft computing. At the first glance, the two methodologies that we discuss are not in common. Rough set construct is the representation of knowledge in terms of attributes, semantic decision rules, etc. On the contrary, genetic programming attempts to automatically create computer programs from a high‐level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both the approaches into a combined system. The challenge is to obtain as much as possible from this association.

It has recently been shown that an approach termed emergence system has gained popularity in a variety of fields, however, emergent computation in decentralized spatially extended systems, such as in cellular automata, is still not well understood. To accept that a system is displaying emergent behavior, the system should be constructed by describing local elementary interactions between components. This is achieved in a different way by describing global behavior and properties of the running system over a period of time. This paper introduces the emergent computational paradigm, and discusses its theoretical formulation using a new general model of cellular automata. We have also developed a technique to study the structure of the state transition of cellular automata in the limit of large system size.