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Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations)

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Knowledge, hidden in voluminous data repositories routinely created and maintained by today’s applications, can be extracted by data mining. The next step is to transform this discovered knowledge into the inference mechanisms or simply the behavior of agents and multi-agent systems. Agent Intelligence Through Data Mining addresses this issue, as well as the arguable challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. This book contains a methodology, tools and techniques, and several examples of agent-based applications developed with this approach. This volume focuses mainly on the use of data mining for smarter, more efficient agents.

Agent Intelligence Through Data Mining is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science.

Data mining (DM) is the process of finding previously unknown, profitable and useful patterns hidden in data, with no prior hypothesis. The objective of DM is to use discovered patterns to help explain current behavior or to predict future outcome. DM borrows concepts and techniques from several long-established disciplines, among them, Artificial Intelligence, Database Technology, Machine Learning and Statistics. The field of DM has, over the past fifteen years, produced a rich variety of algorithms that enable computers to learn from large datasets new relationships/ knowledge.

DM has witnessed a considerable growth of interest over the last five years, which is a direct consequence of the rapid development of the information industry. Data is no longer a scarce resource; it is abundant and it exists, in most of the cases, in databases that are geographically distributed. Most recent advances in Internet and World Wide Web have opened the access to various databases and data resources and, at the same time, they induce many more new problems to make intelligent usage of all data that are both available and relevant. New methods for intelligent data analysis to extract relevant information are needed. The Information Society requires the development of new, more intelligent methods, tools, and theories for the discovering and modeling of relationships in huge amounts of consolidated data warehouses.

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