- Full Description
The authors use a refreshing and novel ‘workbook’ writing style which gives the book a very practical and easy to use feel. It includes methodologies for the development of hybrid information systems, covers neural networks, case based reasoning and genetic algorithms as well as expert systems. Numerous pointers to web based resources and current research are also included. The content of the book has been successfully used by undergraduates around the world. It is aimed at undergraduates and a strong maths background is not required.
- Table of Contents
Table of Contents
- An Introduction to Knowledge Engineering.
- Data, Information and Knowledge.
- Skills of a Knowledge Engineer.
- An Introduction to Knowledge Based Systems.
- Types of Knowledge Based System
- Expert Systems.
- Neural Networks.
- Case Based Reasoning.
- Genetic Algorithms.
- Intelligent Agents.
- Data Mining
- Knowledge Acquisition.
- Knowledge Representation and Reasoning.
- Using Knowledge.
- Logic, Rules and Representation.
- Developing Rule Based Systems.
- Semantic Networks.
- Expert System Shells, Environments and Languages 169.
- Expert System Shells.
- Expert System Development Environments.
- Use of AI Languages.
- Lifecycles and Methodologies.
- The Need for Methodologies.
- Blackboard Architectures.
- Problem Solving Methods.
- HyM (the Hybrid Methodology).
- Building a well Structured Application Using Aion BRE.
- Uncertain Reasoning.
- Hybrid Knowledge.
- Based Systems.
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