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In artificial intelligence it is important to identify the significance of human emotions as a structuring component, their temporal and interactive dynamics for rational decision making and to conduct intelligent interaction with the potential of reasoning. This book provides a systematic approach to design and analyze emotional process and cognitive emotion modeling. The preliminary chapters addresses the logic of grounding emotions in agents for the cross disciplinary audience. The relevance of artificial neural networks for emotion modeling has also been argued regarding its ability for adaptive learning that provides the foundation for the problem identified and leads to the hypotheses devised. A model has been proposed which incorporates hierarchical Self-Organizing-Map technique to address the cooperative and competitive mode of interaction in multi-facet emotion space. This book will be useful for researchers, academicians and psychologists, to explore the underlying neural phenomenon of emotion dynamics and to model these with the help of Artificial Neural Network techniques for agents.

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