System Development

The first step is selecting the "right problem."

pick a problem in which you are already familiar with the domain.
pick a problem that is causing a large number of people, a fair amount of grief.
select a "doable" problem as the first expert system project.
get an expert who is willing to work with you.
have a backup expert.
find out from the expert what is the expected accuracy rate of the prototype.
do not overpromise.
use the rapid prototyping, iterative approach: build a little, test a little.

The next step in the knowledge engineering process is knowledge acquisition.

before interviewing the expert, make sure that you are familiar/comfortable with the domain.
the first session with the expert should be an introductory lecture on the task at hand
have a systematic approach to acquiring knowledge.
incorporate the input and feedback from the expert into the expert system.
pick up manuals and documentation on the subject matter.
tape the knowledge acquisition sessions, if the expert allows.

This involves representing the knowledge in the knowledge base as rules, frames, scripts, semantic networks, or some hybrid.

try to use the representation method which most closely resembles the way the expert is thinking and expressing his/her knowledge.
consider whether uncertainty should play a part in your system.
consider if the expert reasons in a data-driven manner or a goal-directed manner, or both.

remember the motto, "For every shell there is a perfect task, but for every task there is not a perfect shell": remember to first determine the requirements of the task, instead of force-fitting a shell to a task.
try to develop the knowledge base in a modular format for ease of updating.
you need an appropriate and adequate user interface, but concentrate on the knowledge base.
use an incremental, iterative approach to developing your expert system prototype.

for verification and validation of the expert system prototype: check for consistency of the knowledge/logic have a representative set of test cases (hard and soft cases and special subcases) perform backcasting: run the expert system against documented cases and compare the results with historical results use blind verification tests have the expert and other experts test the system.
for evaluation of the expert system prototype: have users evaluate the human factors design of the system (i.e., instructions, free-text comments, ease of updating, exiting capabilities, response time, display and presentation of conclusions, ability to restart, ability for user to offer degree of certainty, graphics, ability to back up to a previous question, etc.).

train the users on how to use the system.
deliver good documentation on the system.
assign a person or group to maintain the system.
instruct the maintenance team on how to maintain the system/use the shell.
don't forget to consider the cost of a runtime license so that multiple copies of the expert system could be used throughout the organization.