MODULE 5
ARTIFICIAL INTELLIGENCE TECHNIQUES
5.1 INTRODUCTION
This module explain some of the AI techniques such as expert systems, case-based reasoning and
fuzzy logic their definitions, benefits and how they are been utilized.
5.2 Expert Systems
Expert systems were developed in the 1970s and were the first large-scale applications of AI in
business and other organizations. They account for an estimated 20 percent of all AI systems today.
5.2.1 Definition:
Expert systems capture the knowledge of individual experts in an organization through in-depth
interviews, and represent that knowledge as sets of rules. These rules are then converted into
computer code in the form of IF-THEN rules. Such programs are often used to develop apps that
walk users through a process of decision making.
5.2.2 Benefit of Expert Systems
Although expert systems lack the robust and general intelligence of human beings, they can
provide benefits to organizations if their limitations are well understood. Only certain classes of
problems can be solved using expert systems.
Expert systems provide the following benefits:
1. Improved decisions,
2. Reduced errors,
3. Reduced costs,
4. Reduced training time, and
5. Better quality and service.
6. They have been used in applications for making decisions about granting credit and for
diagnosing equipment problems, as well as in medical diagnostics, legal research, civil
engineering, building maintenance, drawing up building plans, and educational technology
(personalized learning and responsive testing).
1
, Virtually all successful expert systems deal with problems of classification in limited domains of
knowledge where there are relatively few alternative outcomes and these possible outcomes are
all known in advance. Expert systems are much less useful for dealing with unstructured problems
typically encountered by managers.
Many expert systems require large, lengthy, and expensive development efforts. Hiring or training
more experts may be less expensive than building an expert system. Typically, the environment in
which an expert system operates is continually changing so that the expert system must also
continually change.
Some expert systems, especially large ones, are so complex that in a few years the maintenance
costs equal the development costs. See Figure 5.1 an expert system for credit granting.
Figure 5.1: Rules in an Expert System
An expert system contains a number of rules to be followed. The rules are interconnected;
the number of outcomes is known in advance and is limited; there are multiple paths to the
2
ARTIFICIAL INTELLIGENCE TECHNIQUES
5.1 INTRODUCTION
This module explain some of the AI techniques such as expert systems, case-based reasoning and
fuzzy logic their definitions, benefits and how they are been utilized.
5.2 Expert Systems
Expert systems were developed in the 1970s and were the first large-scale applications of AI in
business and other organizations. They account for an estimated 20 percent of all AI systems today.
5.2.1 Definition:
Expert systems capture the knowledge of individual experts in an organization through in-depth
interviews, and represent that knowledge as sets of rules. These rules are then converted into
computer code in the form of IF-THEN rules. Such programs are often used to develop apps that
walk users through a process of decision making.
5.2.2 Benefit of Expert Systems
Although expert systems lack the robust and general intelligence of human beings, they can
provide benefits to organizations if their limitations are well understood. Only certain classes of
problems can be solved using expert systems.
Expert systems provide the following benefits:
1. Improved decisions,
2. Reduced errors,
3. Reduced costs,
4. Reduced training time, and
5. Better quality and service.
6. They have been used in applications for making decisions about granting credit and for
diagnosing equipment problems, as well as in medical diagnostics, legal research, civil
engineering, building maintenance, drawing up building plans, and educational technology
(personalized learning and responsive testing).
1
, Virtually all successful expert systems deal with problems of classification in limited domains of
knowledge where there are relatively few alternative outcomes and these possible outcomes are
all known in advance. Expert systems are much less useful for dealing with unstructured problems
typically encountered by managers.
Many expert systems require large, lengthy, and expensive development efforts. Hiring or training
more experts may be less expensive than building an expert system. Typically, the environment in
which an expert system operates is continually changing so that the expert system must also
continually change.
Some expert systems, especially large ones, are so complex that in a few years the maintenance
costs equal the development costs. See Figure 5.1 an expert system for credit granting.
Figure 5.1: Rules in an Expert System
An expert system contains a number of rules to be followed. The rules are interconnected;
the number of outcomes is known in advance and is limited; there are multiple paths to the
2
