An expert system is problem solving software that uses rules and a knowledge base to reason through problems in a specific domain, similar to how a human expert would. It consists of a knowledge base containing data and rules, an inference engine that uses the rules to solve problems, and a user interface for humans to input questions. Examples of expert systems include medical diagnosis systems that can identify potential illnesses based on entered symptoms, and animal/plant identification systems that ask questions to narrow down possible results. Advantages are reducing time on common issues and leveraging collective expert knowledge, while disadvantages include high setup costs and need for ongoing updates to stay current.

1. By Ryan Scott

2.  What is an ‘Expert system’?
 Examples of expert systems
 Advantages and disadvantages of
expert systems

3. Shell
 What is an ‘Expert system’?
User interface
 An expert system is a piece of problem solving software
 It differs from other problem solving software in the method it uses
to solve the problem
 They generally use a set of rules particular to the field they are based
on to reason, and eventually solve, the problems given to them
 Additionally, expert systems are designed in 2 key parts:
 Knowledge base, which is made of:
Explanation
 Data
 Rules
 And a ‘Shell’:
Knowledge base
 Containing an explanation system
 Inference engine
 Knowledge base editor
 And a user interface
Data
Rules
system
Inference engine
Knowledge base editor

4.  Medical diagnosis
 Doctors would use the expert system by
inputting the symptoms the patient
has, and the system will check it’s
knowledge base to see what symptoms
match certain illnesses. These will then be
displayed to the doctor as possible
illnesses.
 Animal and plant identification
 The user answers questions presented by
the system to narrow it’s possible results.
Such question would be:
 “Is it most active during the day?”
 Eventually, the user will receive results
based on both their input and the
system’s knowledge base.

5.  Advantages
 Reduces time taken to solve
common issues
 Has collaborative knowledge of
experts, so it is more accurate
than a single experts opinion
 Can predict future problems the
current one could cause
 Disadvantages
 Require a lot of resources to set
up:
 Experts
 Time
 Money
 Has to be kept up to date or it is
redundant
 Staff may need training