Rule-Based Systems in Knowledge Representation: Chapter 04 Overview

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This chapter from the STATERA Library's Volume 02 on Knowledge Representation introduces rule-based systems as a symbolic AI approach that lets computers reach decisions using explicitly defined knowledge and logical conditions. Its core principle is separating the knowledge base, which stores IF-THEN rules, from the inference engine that determines which rules apply to a given set of facts. The text explains forward and backward chaining as the two basic reasoning methods and traces the historical development from expert systems such as MYCIN to modern uses in business process automation and compliance. It highlights interpretability and consistency as key strengths, while noting limitations including the cost of maintaining large rule sets and difficulty handling uncertainty and changing conditions.

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Integrated Knowledge Representation Project Outlines a Reusable Logic Inference Engine

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