ES Design, Development and OperationDr. Ahmed Elfaig

• Knowledge model, knowledge structure, presentation and

organization are the bottleneck of expert system development

• Knowledge model can be graphically illustrated to reflect the

component and integrated nature of different modules of the

problem domain.

• The conceptual model of the problem and the problem sub-module

are shown in the figure below:

Problem Domain and Methods of Assessment

Example of General Knowledge base of ESCNP: Residential Area

Example of General Knowledge base of ESCNP: Residential Area

Example of General Knowledge base of ESCNP: Residential Area

Example of General Knowledge base of ESCNP: Residential Area

Example of General Knowledge base of ESCNP: School Compound

Example of General Knowledge base of ESCNP: School Compound

Example of General Knowledge base of ESCNP: Hospital Area

Example of General Knowledge base of ESCNP: Hospital Area

Example of General Knowledge base of ESCNP: Hospital Area

ES Development Phases

ES Development Phases

• The testing phase aims at showing , validating and verifying the

model and software of ES functions.

• It shows the overall structure of the system and its knowledge

• (verification shows no bugs or technical errors)

• Traces syntax errors that may prevent the rules from firing and fixing

such errors

Goals of Verification

• Make sure there are no:• Bug• Technical errors• Removing errors• Incompleteness• Ambiguity• Inconsistency in system function

Knowledge Acquisition

• Knowledge acquisition : Is processes involve collecting, eliciting, organizing, analyzing and interpreting the knowledge that human experts use when solving particular problem

• Knowledge acquisition involve includes knowledge refinement, validation and verification.

Importance of Knowledge acquisition

Importance of knowledge come from the fact that :

• The power utility of any system depends on underlying knowledge quality

• The clients acceptance of the system depends on the validity of the knowledge it has.

Type of knowledge

• Declarative knowledge: which is used to describe the problem characteristics and concepts

• Heuristic knowledge: Knowledge used to make judgement or strategic rule of thumb.

VALIDATION

• Comparison of research output (knowledge) with the heuristic of expert in the field

• Comparison of the research output with known results

TYPE OF VALIDITY

• Content validity• Criterion validity• Objective validity• Subjective validity

Content Validity

• Results of the system or research test against experts

• The system models test against other models

Criterion validity

• Level of expertise provided by the research or a system

OBJECTIVE VALIDITY

• Actual system Performance• Actual outcome

SUBJECTIVE VALIDITY

• Research results or system performance compare to experts.

VALIDATION PROCESSES

• Known results: for example WHO• Blind performance test: Compare the results

against human experts• Face validation: Qualitative procedure to test

the results• Subjective evaluation: Evaluation of the

results through consultation with experts

Validation: Assessments ResultsParameters consideredMeanSTD

Variable:1.Completeness2.Importance

3.843.72

0.020.01

Output:1. Important results3.960.04

Performance:1.Right results2. Complete results

3.883.8

0.030.02

Explanation;1.Why certain variables are

needed

3.40.03

Field TestingObserved ResultsResearch or system

output Numerical

differences %compliance

60.561.4-0.9-0.015

48.9444.9.10