Knowledge Representation and Reasoning (8hrs) · Introduction to AI

Rule-based (production) systems

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Rule-Based (Production) Systems — Beginner-Friendly Notes

1. What It Is

A production system stores knowledge as if-then rules (productions) and uses an engine to apply them to facts.

Rule: IF condition THEN action / conclude
Example: IF temperature > 30 AND humidity > 80 THEN risk = high

Three parts:

  • Working Memory (WM): current facts (e.g., Temp=32, Humidity=85)
  • Rule Base: long-term if-then rules (domain knowledge)
  • Inference Engine: matches rules against WM, picks one (conflict resolution), fires it to update WM — cycle repeats (recognise-act cycle).
loop:
  matches = rules whose IF matches WM
  if none: stop
  pick one rule (conflict resolution: specificity, recency, priority)
  fire it: add/modify facts in WM

4. Forward vs Backward with Rules

  • Data-driven (forward): start from facts, fire any matching rule → derive all conclusions (typical for production systems, e.g., monitoring).
  • Goal-driven (backward): used when querying, as in Prolog — start from query, find rules that conclude it.

5. Advantages

Advantage Why
Modular Add/remove one rule without rewriting others
Readable Rules look like expert statements → good for expert systems
Explainable Can show chain "R1 and R2 fired because..."

6. Limitations (exam point)

  • Brittle: if facts incomplete, no rule fires; cannot handle uncertainty (needs certainty factors — Module 4).
  • Conflict resolution is heuristic — wrong priority → wrong conclusion.
  • Rule explosion: large KBs become hard to maintain, may contain redundant/conflicting rules → needs validation.
  • Inefficient without indexing (matching many rules against WM = Rete algorithm in practice).

7. Connection to Syllabus History

  • 1980s expert systems (MYCIN, DENDRAL, XCON) were production systems — first commercial AI, later fell due to maintenance cost and lack of learning.

8. One-Line Summary

A production system stores domain knowledge as if-then rules and repeatedly matches, selects and fires them against working memory via an inference engine, providing modular, explainable reasoning but suffering from brittleness, rule explosion and lack of uncertainty handling.

9. Quick Self-Check

  1. Name the three parts of a production system.
  2. Why is conflict resolution needed?
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