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The book uses ideas from machine learning and artificial intelligence to provide the models of emergent system. This task falls into an extensive category of hybrid complex systems, which, broadly speaking, can be said to be the science of constructing models that describe the emergence; where we will introduce new approaches based on rough sets combined with neural networks, genetic programming, and multi-agents. Moreover, the constructions of some cellular systems, in which cellular automata will be integrated with rough sets and multi-agents, are presented also. The main value of the study is that it provides an illustration of how simple learning processes may lead to the formation of the agent behavior, which can give an emergence to the system. By allowing the task environment to be an integral component of the problem-solving algorithm, all the natural constraints, including those too subtle for the knowledge engineer to extract, are available to the algorithms and emerge at appropriate moments while solving problems.