Characteristics:

Symbolic logic
An explicit knowledge base understandable to professionals of the field
Ability to explain its conclusions that are meaningful to its user

Expert systems are used in two different ways:

Decision support: Reminding information or options to an experienced decision maker. Commonly used in medicine.
Decision making: Allowing an unqualified person to make a decision beyond his or her level or training or expertise. Commonly used in industrial systems.

Purpose for the Construction of Expert Systems:

Recording and distributing scarce expert knowledge
Applying the expert knowledge to remote locations
Ensuring the quality of problem solving
Training experts out of ordinary people

Recognizing problems
Recognizing solutions
Explaining the choice of solution
Selecting applicable solutions
Dealing with incomplete information
Restructuring problems
Reducing the need for research

Common Nature of Expert System's Tasks:

When experts out perform non experts
When the task requires reasoning and knowledge instead of intuition or reflex
When the task could be done either in minutes or hours
When the task could be encoded
When the task is commonly instructed to beginners of the field

Expert System Reasoning:

Forward Chaining: Data oriented
Existing facts matched to rule antecedents
Matching rules result in consequent: conclusions
Backward Chaining: Goal oriented
Select goal or conclusion; match to rule-consequents
Checking for match between rule-antecedents and facts
Repeat until conclusion matches fact
Conflict Resolution: Select rule to applicate
Specificity, simplicity, random

Development of Expert Systems Involves:

Knowledge Acquisition
Knowledge Representation
Knowledge Encoding
Expert System Testing
Expert System Implementation