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Unit 4 — Introduction to Expert Systems

Lesson 30 of 34 in the free Artificial Intelligence notes on Siksha Sarovar, written by Rohit Jangra.

Expert Systems: Introduction

An expert system is an AI program that emulates the decision-making ability of a human expert in a specific, narrow domain, by combining a large body of domain knowledge with an inference mechanism that reasons over it.

Architecture of an Expert System

ComponentRole
Knowledge baseStores domain facts and rules (heuristics) provided by human experts
Working memoryHolds facts specific to the current case/consultation
Inference engineApplies the rules to the facts to derive new conclusions (forward or backward chaining)
Explanation facilityAnswers "why?" / "how?" — justifies conclusions in human-understandable terms
Knowledge acquisition moduleSupports adding/refining knowledge, typically with help from a "knowledge engineer" who interviews the domain expert
User interfaceManages the dialogue between the user and the system

Forward vs Backward Chaining

Forward chaining (data-driven)Backward chaining (goal-driven)
Starts fromKnown factsA hypothesis/goal to prove
ProcessFire any rule whose conditions are satisfied, adding new facts, repeatWork backward: to prove the goal, find a rule that concludes it, then try to prove its conditions
Good forMonitoring/diagnosis where many facts arrive continuouslyDiagnosis/consultation where the goal is known and facts are gathered on demand
Example systemProduction systems monitoring a factoryMYCIN-style medical diagnosis

Why Expert Systems (and Why Now, in the Syllabus)

Expert systems are the natural application of everything covered in Units 1–3: production systems (Unit 1) provide the rule-firing mechanism, knowledge representation and predicate logic (Unit 2) provide the way facts and rules are stored, and learning techniques (Unit 3) can be used to refine the knowledge base over time.

Classic Historical Expert Systems

SystemDomainSignificance
MYCINDiagnosing bacterial infections, recommending antibioticsPioneered certainty-factor reasoning for uncertain medical knowledge
DENDRALInferring molecular structure from mass-spectrometry dataOne of the first successful expert systems (1965)
XCON (R1)Configuring VAX computer ordersOne of the first commercially deployed, profitable expert systems

Advantages and Limitations

AdvantagesLimitations
Encodes scarce expert knowledge so it's widely, consistently availableKnowledge acquisition (interviewing experts, encoding rules) is slow and expensive
Can explain its reasoning step by stepBrittle outside its narrow domain — no common sense beyond what was explicitly encoded
Consistent — doesn't have off days like a human expertMaintaining/updating a large rule base becomes difficult as it grows