Constructing Intelligent Agents Using Java, 2nd edition (John Wiley & Sons, 2001)

This book is divided into two major parts. Part 1 focuses on the artificial intelligence algorithms and techniques used to make applications and agents intelligent. Part 2 builds on Part 1 by taking the AI algorithms and using them in an intelligent agent framework and example applications.

In Chapter 1, we discuss some of the history of artificial intelligence research and the basic premises of both the symbol processing and neural network (connectionist) schools. In Chapter 2, we show how to solve problems using search and state-based definitions of the world. Chapter 3 deals with the major types of knowledge representation used in AI systems. Reasoning systems, specifically rule-based systems, are the focus of Chapter 4. In Chapter 5, we discuss learning and adaptive techniques and the advantages such behavior provides in intelligent agents. Chapter 6 provides a bridge from the artificial intelligence techniques described in Chapters 2 through 5 to the intelligent agents and the applications developed in the rest of the book. We look at the agent attributes of perception and action and how AI provides the solutions. The issues related to multi-agent systems are also discussed.

In Part 2, we change our focus from the underlying artificial intelligence issues and techniques to their application to major intelligent agent paradigms. Chapter 7 is a key chapter in this book, where we develop the CIAgent intelligent agent architecture, going from requirements through specifications and design. Chapter 8 illustrates how we can use our CIAgent framework to construct an application that provides a simple agent platform, allowing users to develop and plug-in their own agents. The Internet is the focus of our intelligent agent application in Chapter 9. Chapter 10 focuses on the issues involved when autonomous agents interact in multiagent systems. We conclude in Chapter 11 with an examination of several Java-based agent environments and applications.

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Data Mining with Neural Networks (McGraw Hill, 1996)

This book is intended for information systems managers and others who want to apply neural network technology and the techniques of data mining to solve common business problems such as forecasting sales and inventory patterns and analysing customer profiles. It includes concrete implementation strategies, reinforced with real-world business examples and a minimum of formulas, along with case studies drawn from a broad range of industries. The book illustrates the popular data mining functions of classification, clustering, modeling, and time-series forecasting through examples developed using the IBM Neural Network Utility.

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