Expert Systems Questions and Answers

Q: DENDRAL rules are used to determine :

DENDRAL rules are used to determine complex chemical structures.DENDRAL, developed at Stanford from 1965 by Edward Feigenbaum, Joshua Lederberg and Bruce Buchanan, is regarded as the first expert system. It inferred the molecular structure of unknown organic compounds from mass spectrometry data, using heuristic rules supplied by chemists.It was not built for pharmaceutical, biological or geological structures; it worked out the structure of chemical molecules in general.

Q: Who is Domain expert ?

Of the options, the domain expert is the Doctor.In building an expert system, the domain expert is the person with deep knowledge of the problem area whose expertise is to be captured. In a medical diagnosis system such as MYCIN, that is the physician. A knowledge engineer interviews the expert and encodes the knowledge as rules.The software engineer and the programmer build the system, and the operator runs it; none of them supplies the domain knowledge.

Q: Which of the following statements is false for fuzzy expert systems ?

The false statement is Standard expert systems are always more appropriate than fuzzy expert systems.Fuzzy expert systems are designed for problems involving vague or imprecise knowledge, such as 'temperature is high', where crisp rules work poorly. Which kind of system is more appropriate depends on the problem, so 'always' makes the statement false.The other statements describe how a fuzzy expert system is actually built: first choose the linguistic variables, then generate fuzzy rules from the expert's knowledge using those variables, and build the system from that rule set with fuzzy inference.

Q: Which component of an expert system holds the domain rules?

The knowledge base stores the domain expertise as rules, frames or other structures, separate from the machinery that uses it. The inference engine applies that knowledge, working memory holds the facts of the current case, and the explanation facility reports the reasoning afterwards. This separation of knowledge from control is the defining architectural commitment of the field, and it is what makes a shell reusable across domains. Most errors here come from carrying a result across from a neighbouring case where it does not hold.

Q: Which component decides which rule to apply next?

The inference engine matches rules against working memory, resolves the resulting conflict set and fires the chosen rule. The knowledge base merely stores rules and imposes no order of its own. The interface handles dialogue and the acquisition module handles editing. Keeping the control strategy in the engine is precisely what allows a knowledge base to be written declaratively, without its author thinking about execution order at all. Working one numerical case through both cases side by side fixes the distinction better than any rule of thumb.

Q: Which store holds the facts of the case being solved?

Working memory holds the assertions specific to the current consultation and is emptied between cases, while the knowledge base persists. The distinction between long-term knowledge and short-term facts mirrors human problem solving and is what makes the same rules serve every case. A rule index is an implementation detail and the trace is a by-product. Confusing the two stores is the commonest beginner's error in this topic. A single measurement chosen to respond to the one asymmetry beats several that respond to shared behaviour.

Q: Which form does a production rule take?

A production rule pairs a condition part matched against working memory with an action part that asserts, retracts or acts. It is deliberately less expressive than full predicate logic, which is what keeps matching efficient. Frames, implications and semantic networks are alternative representations covered elsewhere in this topic. The modularity of rules, each independently meaningful, is the property that made knowledge bases maintainable.

DENDRAL rules are used to determine :

A.Pharmaceutical compound structures
B.Biological structures
C.Geological structures
D.Complex chemical structures

Exam Year(s)

MH SET - 2018, SET Exam - 2018,

Answer: D

DENDRAL rules are used to determine complex chemical structures.

DENDRAL, developed at Stanford from 1965 by Edward Feigenbaum, Joshua Lederberg and Bruce Buchanan, is regarded as the first expert system. It inferred the molecular structure of unknown organic compounds from mass spectrometry data, using heuristic rules supplied by chemists.

It was not built for pharmaceutical, biological or geological structures; it worked out the structure of chemical molecules in general.

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Who is Domain expert ?

A.Software Engineer
B.Operator
C.Doctor
D.Programmer

Exam Year(s)

MH SET - 2018, SET Exam - 2018,

Answer: C

Of the options, the domain expert is the Doctor.

In building an expert system, the domain expert is the person with deep knowledge of the problem area whose expertise is to be captured. In a medical diagnosis system such as MYCIN, that is the physician. A knowledge engineer interviews the expert and encodes the knowledge as rules.

The software engineer and the programmer build the system, and the operator runs it; none of them supplies the domain knowledge.

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Which of the following statements is false for fuzzy expert systems ?

A.First stage in building a fuzzy expert system is choosing suitable linguistic variables
B.Fuzzy expert system is built by creating a set of fuzzy rules applying fuzzy inference
C.Standard expert systems are always more appropriate than fuzzy expert systems.
D.Fuzzy rules are generated based on expert's knowledge, using linguistic variables.

Exam Year(s)

MH SET - 2018, SET Exam - 2018,

Answer: C

The false statement is Standard expert systems are always more appropriate than fuzzy expert systems.

Fuzzy expert systems are designed for problems involving vague or imprecise knowledge, such as 'temperature is high', where crisp rules work poorly. Which kind of system is more appropriate depends on the problem, so 'always' makes the statement false.

The other statements describe how a fuzzy expert system is actually built: first choose the linguistic variables, then generate fuzzy rules from the expert's knowledge using those variables, and build the system from that rule set with fuzzy inference.

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Which amongst the following are essential components of an expert system ?

(i) Inference engine
(ii) Context graph
(iii) Knowledge base
(iv) Computer vision

A.(i) and (ii)
B.(i) and (iii)
C.(i), (ii) and (iii)
D.(ii), (iii) and (iv)

Exam Year(s)

MH SET - 2021, SET Exam - 2021,

Answer: B

The essential components are (i) and (iii), so the answer is (i) and (iii).

An expert system is built from a knowledge base, holding the facts and rules elicited from human experts, and an inference engine, which applies those rules to the facts of a case by forward or backward chaining to reach conclusions. A user interface and an explanation facility are usually added.

A context graph is not a standard component, and computer vision is a separate field of AI that an expert system does not need. Options including (ii) or (iv) are therefore wrong.

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An expert system shell is an expert system without

A.domain knowledge
B.explanation facility
C.reasoning with knowledge
D.all of the above

Exam Year(s)

UGC NET - 2012,

Answer: A

An expert system shell is an expert system without domain knowledge. The shell has the inference engine, the explanation facility and the user interface, and it becomes an expert system when the knowledge base of a particular field is added. So the part that is missing from the shell is the domain knowledge. The shell still has the explanation facility and the ability to reason with knowledge. The keyed answer is domain knowledge.

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Which of the following is not a part of an expert system shell ?

A.Knowledge Base
B.Inference Engine
C.Explanation Facility
D.None of the above

Exam Year(s)

UGC NET - 2013,

Answer: A

The knowledge base is not a part of an expert system shell. A shell is an expert system with the domain knowledge removed: it supplies the inference engine, the explanation facility and the user interface, and the developer fills in the knowledge base for a particular problem.

The inference engine and the explanation facility are components of the shell, so options B and C are parts of it. The option 'none of the above' is wrong because there is a part, the knowledge base, that is not included.

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Which component of an expert system holds the domain rules?

A.The knowledge base
B.The inference engine, which applies the rules to the facts supplied by the user
C.The working memory
D.The explanation facility, which reports how a conclusion was reached

Answer: A

The knowledge base stores the domain expertise as rules, frames or other structures, separate from the machinery that uses it. The inference engine applies that knowledge, working memory holds the facts of the current case, and the explanation facility reports the reasoning afterwards. This separation of knowledge from control is the defining architectural commitment of the field, and it is what makes a shell reusable across domains. Most errors here come from carrying a result across from a neighbouring case where it does not hold.

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Which component decides which rule to apply next?

A.The knowledge acquisition module, which adds new rules as they are discovered
B.The inference engine
C.The knowledge base, which stores the rules in the order they should be tried
D.The user interface

Answer: B

The inference engine matches rules against working memory, resolves the resulting conflict set and fires the chosen rule. The knowledge base merely stores rules and imposes no order of its own. The interface handles dialogue and the acquisition module handles editing. Keeping the control strategy in the engine is precisely what allows a knowledge base to be written declaratively, without its author thinking about execution order at all. Working one numerical case through both cases side by side fixes the distinction better than any rule of thumb.

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Which store holds the facts of the case being solved?

A.The rule index
B.The explanation trace, which records each rule as it fires during the session
C.Working memory
D.The knowledge base, which is loaded afresh at the start of every consultation

Answer: C

Working memory holds the assertions specific to the current consultation and is emptied between cases, while the knowledge base persists. The distinction between long-term knowledge and short-term facts mirrors human problem solving and is what makes the same rules serve every case. A rule index is an implementation detail and the trace is a by-product. Confusing the two stores is the commonest beginner's error in this topic. A single measurement chosen to respond to the one asymmetry beats several that respond to shared behaviour.

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10.

Which form does a production rule take?

A.A frame with slots that inherit their default values from a parent frame
B.A logical implication with quantifiers
C.A network of nodes joined by labelled links representing relationships
D.An IF condition THEN action pair

Answer: D

A production rule pairs a condition part matched against working memory with an action part that asserts, retracts or acts. It is deliberately less expressive than full predicate logic, which is what keeps matching efficient. Frames, implications and semantic networks are alternative representations covered elsewhere in this topic. The modularity of rules, each independently meaningful, is the property that made knowledge bases maintainable.

Discuss in Forum

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