HRM 3440 Lecture Notes - Lecture 3: Decision Support System, Inference Engine, Expert System
23 May 2018
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HRM 3440 Lecture 3 Notes – Principle: Expert systems can enable a novice to perform at the
level of an expert but must be developed and maintained very carefully.
Introduction
• Learning systems use a combination of software and hardware to allow a computer to
change how it functions or reacts to situations based on feedback it receives (e.g., a
computerized chess game).
• A neural network is a computer system that can simulate the functioning of a human
brain (e.g., disease diagnostics system).
• A genetic algorithm is an approach to solving large, complex problems in which a
number of related operations or models change and evolve until the best one emerges.
• The approach is based on the theory of evolution, which requires variation and natural
selection.
• Intelligent agents consist of programs and a knowledge base used to perform a specific
task for a person, a process, or another program.
• An expert system consists of a collection of integrated and related components,
including a knowledge base, an inference engine, an explanation facility, a knowledge
acquisition facility, and a user interface.
• The knowledge base is an extension of a database, discussed and an information and
decision support system, discussed
• It contains all the relevant data, rules, and relationships used in the expert system.
• The rules are often composed of IF-THEN statements, which are used for drawing
conclusions.
• The inference engine processes the rules, data, and relationships stored in the
knowledge base to provide answers, predictions, and suggestions the way a human
expert would.
• The explanation facility of an expert system allows the user to understand what rules
were used in arriving at a decision.
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