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Book Contents

Ch. 10
Building Knowledge-Driven DSS and Mining Data

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Other Important Terms

Some artificially intelligent decision aids act for managers and are called agents. An agent is a self-contained program that runs in the background on a client or server and performs useful functions for a specific owner. Agents may monitor exceptions based on criteria or execute automated tasks. For example, once an event occurs an agent performs a pre-defined action and then it returns to a monitoring state. An agent might monitor sales goals or levels of product defects. Agents can be incorporated in a variety of DSS. The term is important to know, but how an agents works is beyond the purpose of this book.

A development environment is used by a Knowledge-Driven DSS designer and builder. A development environment typically includes software for creating and maintaining a knowledge base and software called an inference engine. An inference engine reasons with a set of rules created by a developer.

A domain expert is a key person in a Knowledge-Driven DSS development project. A domain expert is the person who has expertise in the domain in which a specific system is being developed. A domain expert works closely with a knowledge engineer to capture the expert's knowledge in a knowledge base. This process is used especially for capturing rule and relationship information in a computer readable format.

Knowledge acquisition is the extraction and formulation of knowledge derived from various sources, especially from experts. A knowledge base is a collection of organized facts, rules, and procedures. A knowledge base has a description of the elements in the process along with their characteristics, functions, and relationships. It also contains rules about the actions to implement as a result of certain events. A knowledge base can also obtain its information from external programs and databases. When dealing with a particular task or problem, a Knowledge-Driven DSS constructs a number of hypotheses based on the external information supplied, its own knowledge, and the rules in its knowledge base.

As the above paragraphs amply demonstrate, artificial intelligence researchers have an extensive technical jargon that managers and MIS professionals must have some familiarity with if they want to build Knowledge-Driven DSS. The key to success is learning some of the jargon, and staying focused on the broader objective of building decision support systems that use software with "reasoning" capabilities.

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