Editing Symbolic AI and Expert Systems (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Evaluating symbolic AI systems requires different approaches than evaluating ML models:

'''Completeness''': Does the rule base cover all scenarios in the domain? Measure by presenting the system with diverse domain scenarios and checking for "no conclusion reached" failures.

'''Consistency''': Are there rules that could produce contradictory conclusions for the same input? Formal verification tools can check this exhaustively.

'''Coverage vs. precision''': Measure recall (what fraction of correct conclusions does the system reach?) vs. precision (what fraction of the system's conclusions are correct?).

'''Explanation quality''': Do the explanations generated by the system match the reasoning a domain expert would use? Evaluate with expert review.

Expert practitioners combine symbolic evaluation with ablation — systematically removing or disabling individual rules and measuring the impact on overall system performance, identifying which rules carry the most diagnostic weight.
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