Editing Symbolic AI and Expert Systems (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Symbolic AI vs. Neural AI Comparison
! Property !! Symbolic AI !! Neural AI (ML)
|-
| Data requirements || Low (rules are hand-coded) || High (needs training data)
|-
| Interpretability || Full (every rule inspectable) || Low (black box)
|-
| Robustness to noise || Poor (strict rule matching) || High (learned tolerance)
|-
| Generalization || Poor (combinatorial explosion) || High (pattern generalization)
|-
| Formal verification || Possible || Very difficult
|-
| Domain expertise required || Very high (knowledge engineers) || Moderate (ML engineers + data)
|-
| Handling uncertainty || Limited (fuzzy logic, certainty factors) || Natural (probabilistic outputs)
|}

'''Failure modes:'''
* '''Knowledge acquisition bottleneck''' — Encoding an expert's knowledge into rules is enormously time-consuming. Even experts cannot always articulate their reasoning explicitly.
* '''Brittleness''' — Rules fail on inputs not explicitly anticipated. A medical rule might fail on a patient with an atypical presentation. Neural networks generalize; rule systems cannot.
* '''Rule conflict''' — As rule bases grow, rules can produce contradictory conclusions. Conflict resolution strategies (priority, specificity, recency) add complexity.
* '''Maintenance burden''' — As the domain evolves, the rule base must be updated. In fast-moving domains, this becomes unsustainable.
* '''Closed-world assumption''' — Classical symbolic systems assume anything not known is false. This breaks in open-world settings.
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