Page 179 - DCAP506_ARTIFICIAL_INTELLIGENCE

Unit 13: Expert Systems and its Architecture

13.9 Speech Recognition Notes
13.10 Summary
13.11 Keywords
13.12 Review Questions

13.13 Further Readings

Objectives

After studying this unit, you will be able to:
 Understand the concept of expert system

 Discuss the components of expert system
 Illustrate the characteristics of ES
 Understand the expert system architecture
 Discuss the applications of expert system

 Explain the concept of ES shells
 Understand the concept of dealing with uncertainty

Introduction

An expert system is software that attempts to reproduce the performance of one or more human
experts, most commonly in a specific problem domain, and is a traditional application and/or
subfield of artificial intelligence. A wide variety of methods can be used to simulate the
performance of the expert however common to most or all are (1) the creation of a so-called
“knowledge base” which uses some knowledge representation formalism to capture the Subject
Matter Experts (SME) knowledge and (2) a process of gathering that knowledge from the SME
and codifying it according to the formalism, which is called knowledge engineering. Expert
systems may or may not have learning components but a third common element is that once the
system is developed it is proven by being placed in the same real world problem solving
situation as the human SME, typically as an aid to human workers or a supplement to some
information system. As a premiere application of computing and artificial intelligence, the
topic of expert systems has many points of contact with general systems theory, operations
research, business process reengineering and various topics in applied mathematics and
management science.

13.1 Part of Expert System

13.1.1 Main ES Components

 Knowledge base
 contains essential information about the problem domain
 often represented as facts and rules

 Inference engine
 mechanism to derive new knowledge from the knowledge base and the

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