Developing Social Informational Network Models Based on Evolutionary Computation and Machine Learning Theories

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The objective of the research project is to develop agent-based models to uncover the sophisticated and emergent properties of modern social information networks such as the Internet. We have utilized both evolutionary computation and machine learning theories in the framework of multiagent-based complex adaptive system approach. The main results are summarized as follows :(1) Organizational Learning Oriented Classifier System (OCS)OCS is novel extension of conventional Learning Classifier Systems in the GA literature to multiagent and organizational learning environments.(2) Inverse Simulation Techniques and its Application to Social Agent-based Simulator TRURLInverse Simulation is a novel technique to analyze phenomena in complex simulation tasks. This is attained by new GAs for multimodal and multiobjective function optimization.(3) Application of the Techniques to Practical Task DomainsThe developed methods are applied to practical task domains : Printed Circuit Board Parts Placement, Fairy-Wing : Guidance System with Smart IC-Cards, Coevolution in Ecomarketing Simulation, Knowledge Management and Social Interaction Studies.