1. Design of Expert Systems
Software Engineering methodology for developing practical ES
ESDLC
General Stages
Feasibility study
Rapid Prototyping
Alpha system (in-house verification)
Beta system (tested by users)
Maintenance and Evolution

2. Planning Phase Feasibility Assessment
Resource Allocation (HR, time, financial)
Task Phasing and Scheduling
Determine high-level requirements

3. Knowledge Acquisition
Most important stage in the development of ES
During this stage the knowledge engineer works with the domain expert to acquire, organize and analyze the domain knowledge for the ES.
Knowledge acquisition bottleneck
Acquisition from expert
Define strategy (consider various options)
Identify concrete knowledge elements
Group and classify knowledge. Develop hierarchical representation where possible

4. Knowledge Acquisition (Cont.)
Identify knowledge source. Expert in the domain
Identify potential sources (human expert, expert handbooks/manuals), e.g. car mechanic expert system’s knowledge engineer may chose a mix of interviewing an expert mechanic and using a mechanics trouble-shooting manual.
Tip:
Limit the number of knowledge sources (experts) for simple domains to avoid scheduling and view conflicts
However, a single expert approach may only be applicable to restricted small domains
Rank by importance
Rank by availability
Select expert/panel of experts
If more than one expert has to be consulted, consider a blackboard system. More than one knowledge source (kept partitioned), interact through interface called Blackboard

5. Knowledge Elicitation
Getting knowledge from the expert knowledg elicitation vs. broader term knowledge acquisition
Elicitation methods may be broadly divided into:
Direct Methods
Interviews
Very good at initial stages
Balance between structured (multiple choice, rating scale) and un-structured interviewing)
Record interviews (transcribe or tape)
Mix of open and close ended questions
Informal Discussions (gently control digression, but do not offend expert by frequent interruption)
Indirect Methods
Questionnaire

6. Problems with Elicitation
not be able to effectively articulate her knowledge
provide irrelevant information.
Provide incomplete knowledge
Provide inconsistent or incorrect knowledge

7. Knowledge Acquisition Techniques
Knowledge elicitation by interview
Brainstorming session with one or more experts
Introduce some structure
Define problem at hand
Prompt for ideas
Looks for converging lines of though
Electronic brainstorming
On-site observation
Documented organizational expertise, e.g. troubleshooting manuals

8. Knowledge Analysis
Analyze and structure the knowledge gained during the knowledge acquisition phase
Identify specific knowledge elements
From the notes taken during the interview sessions, extract specific
Strategies (as a list of points)
Translate to rules
Heuristics
Identify concepts
Represent concepts and their relationships using cognitive maps

9. Cognitive Map Example Age Patient Medical History Gets Personal Tests
Blood
Sugar
hematology
Echo
Cardiogram
Gets

10. Knowledge Analysis (Cont.)
Inference networks
Graphical representation of system’s rules
Diagnosis is Anemia
Inner surface of eyes lids pale
Consistent Low Blood Pressure
Feeling Listless
Symptoms indicate anemia
Blood test shows low hemoglobin level OR
AND

11. Knowledge Analysis (Cont.)
Flowchart
Sequence of steps
Depict the order of application of rule sets
e.g. Doctor begins by asking symptoms
If not indicative of some disease the doctor will not ask for specific tests.
If symptomatic of two or three potential diseases
The doctor decided disease to check for first
Rules out potential diagnoses in some heuristic sequence

12. Knowledge Design At the end of this phase, we have
Knowledge definition
Detailed design
Decide how to represent knowledge
Rules and Logic
Frames
Decide a development tool. Consider whether it supports your planned strategy.
Internal fact structure
Mock interface

13. Code Coding Prepare tests Comment code Develop User’s manual.
Installation guide
At the end of this phase the system is ready to be tested.

14. Linear Model for ES development
Planning
Knowledge Acquisition and Analysis
Work Plan
Knowledge Baseline
Knowledge Design
Design Baseline
Code
Knowledge Verification
System Evaluation
Encoding of knowledge
Using a development tool
Formal in-house testing
Product evaluation by users