Expert Systems Questions and Answers

Q: Which trade does a deep model impose in exchange for coverage?

Building a causal model of a device demands more effort than eliciting associations, and simulating it costs more at run time, which is why practical systems often combine a shallow layer for common faults with a deep layer for the rest. Explanation actually improves with a model, modularity is unaffected, and stored cases belong to case-based reasoning. The hybrid is the usual engineering answer. Framing the question as which quantity the change acts upon makes the answer follow without calculation.

Q: Which characteristic makes a domain suitable for an expert system?

The classic criteria are that genuine experts exist, that they outperform novices, that a case takes minutes to hours rather than seconds or months, and that the knowledge is largely cognitive. Rapid change makes maintenance impossible, common sense is exactly what these systems lack, and interaction is normally essential. Checking a domain against this list before building was standard practice and remains sound advice. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

Q: Which task is a poor candidate for an expert system?

Common sense is the one thing rule bases have never captured, since it is enormous, mostly unstated and resistant to enumeration. The other descriptions name favourable conditions: narrow scope, clear inputs and expert agreement all help. The Cyc project's decades of effort at encoding common sense by hand is the measure of how hard the excluded case actually is.

Q: Which project attempted to encode common sense by hand?

Cyc set out in 1984 to hand-encode millions of common-sense assertions, on the argument that no learning system could bootstrap without them. It is the largest such effort ever attempted and its partial success is still debated. CLIPS and EMYCIN are shells and PROSPECTOR is a geological system. Cyc's premise, that brittleness is a knowledge problem rather than an algorithm problem, is what makes it belong here. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Q: Which characteristic distinguishes an expert system from conventional software?

Conventional programs interleave knowledge and control in procedural code, while an expert system keeps the domain knowledge declarative and lets a general engine apply it. That separation is what enables explanation, incremental authoring and shell reuse. Language choice is incidental, speed is usually worse, and validation guarantees nothing of the kind. Every other characteristic of these systems follows from this one. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Q: Which property allows an expert system to be extended incrementally?

Because a rule states a relation rather than a step in a procedure, adding one does not require rewriting anything else, which is what let knowledge bases grow through use. Recompilation, implementation language and regression testing are engineering practices rather than the enabling property. The qualification is that interaction through working memory erodes the independence as the base grows. A single measurement chosen to respond to the one asymmetry beats several that respond to shared behaviour.

Q: Which representation suits knowledge stated as a small fixed set of conditions and actions?

A decision table lists every combination of a few conditions against the action each requires, which is compact, easy to check for completeness and easy to convert into rules. Frames, networks and blackboards address structure, relations and cooperation. The table's chief virtue is that missing and contradictory combinations are visible at a glance, which is exactly what verification of a rule base struggles to achieve. Framing the question as which quantity the change acts upon makes the answer follow without calculation.

Q: Which advantage does a decision table have over an equivalent rule set?

Every combination of conditions appears as a column, so a gap or a contradiction is immediately visible, whereas the same defect hides easily in a list of rules. Expressive power is lower rather than higher, speed is comparable and evaluation order is not the point. Tables are therefore often used as the authoring medium and compiled into rules for execution.

Q: Which term describes a set of rules that leads back to its own starting condition?

Circular rules can drive an engine into an unterminating loop, or at best waste effort rederiving what is already known. Refractoriness limits the damage but does not remove the defect. Subsumption, redundancy and unreachability are the other three classical faults that static verification looks for. Detecting circularity amounts to finding a cycle in the graph of rule dependencies.

Q: Which structure does forward chaining most naturally build?

Forward chaining keeps asserting conclusions into working memory until the rules are exhausted, so what accumulates is a set of derived facts. The goal-rooted proof tree belongs to backward chaining, frontiers to search and dependency networks to truth maintenance. Because everything derivable is derived, forward chaining can do a great deal of irrelevant work when only one conclusion was wanted. Reading the definition literally, rather than recalling a formula, is the reliable route through questions of this shape.

51.

Which trade does a deep model impose in exchange for coverage?

A.Loss of modularity
B.The need to collect a very large number of solved cases before the system can be used at all
C.Greater cost to build and to run
D.Loss of the ability to explain the reasoning in terms the domain expert would recognise

Answer: C

Building a causal model of a device demands more effort than eliciting associations, and simulating it costs more at run time, which is why practical systems often combine a shallow layer for common faults with a deep layer for the rest. Explanation actually improves with a model, modularity is unaffected, and stored cases belong to case-based reasoning. The hybrid is the usual engineering answer. Framing the question as which quantity the change acts upon makes the answer follow without calculation.

Discuss in Forum

52.

Which characteristic makes a domain suitable for an expert system?

A.The domain changes rapidly enough that no fixed body of knowledge would remain valid for long
B.The task requires common sense
C.Solutions must be produced without the possibility of asking the user any questions at all
D.Recognised human experts solve it in bounded time

Answer: D

The classic criteria are that genuine experts exist, that they outperform novices, that a case takes minutes to hours rather than seconds or months, and that the knowledge is largely cognitive. Rapid change makes maintenance impossible, common sense is exactly what these systems lack, and interaction is normally essential. Checking a domain against this list before building was standard practice and remains sound advice. Checking the units of each quantity catches a wrong formula before the arithmetic begins.

Discuss in Forum

53.

Which task is a poor candidate for an expert system?

A.One needing broad common sense
B.One in which a single narrow decision is made repeatedly by a small number of specialists
C.One with clear inputs
D.One where the experts agree closely with one another about how each case should be handled

Answer: A

Common sense is the one thing rule bases have never captured, since it is enormous, mostly unstated and resistant to enumeration. The other descriptions name favourable conditions: narrow scope, clear inputs and expert agreement all help. The Cyc project's decades of effort at encoding common sense by hand is the measure of how hard the excluded case actually is.

Discuss in Forum

54.

Which project attempted to encode common sense by hand?

A.PROSPECTOR, which combined geological evidence using an inference network of likelihood ratios
B.Cyc
C.CLIPS, which provided a portable forward-chaining shell for building rule-based applications
D.EMYCIN

Answer: B

Cyc set out in 1984 to hand-encode millions of common-sense assertions, on the argument that no learning system could bootstrap without them. It is the largest such effort ever attempted and its partial success is still debated. CLIPS and EMYCIN are shells and PROSPECTOR is a geological system. Cyc's premise, that brittleness is a knowledge problem rather than an algorithm problem, is what makes it belong here. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Discuss in Forum

55.

Which characteristic distinguishes an expert system from conventional software?

A.It always runs faster
B.It cannot produce an incorrect answer once the knowledge base has been properly validated
C.Knowledge is separated from the control that uses it
D.It is written in a specialised language that only a knowledge engineer is able to read easily

Answer: C

Conventional programs interleave knowledge and control in procedural code, while an expert system keeps the domain knowledge declarative and lets a general engine apply it. That separation is what enables explanation, incremental authoring and shell reuse. Language choice is incidental, speed is usually worse, and validation guarantees nothing of the kind. Every other characteristic of these systems follows from this one. The quantity that varies and the quantity that does not are worth naming before any substitution is attempted.

Discuss in Forum

56.

Which property allows an expert system to be extended incrementally?

A.The inference engine recompiles the whole knowledge base whenever any rule has been changed
B.The shell is written in C
C.Each new rule is tested against the complete set of stored cases before it is finally accepted
D.Rules are declarative and independent

Answer: D

Because a rule states a relation rather than a step in a procedure, adding one does not require rewriting anything else, which is what let knowledge bases grow through use. Recompilation, implementation language and regression testing are engineering practices rather than the enabling property. The qualification is that interaction through working memory erodes the independence as the base grows. A single measurement chosen to respond to the one asymmetry beats several that respond to shared behaviour.

Discuss in Forum

57.

Which representation suits knowledge stated as a small fixed set of conditions and actions?

A.A decision table
B.A frame hierarchy, in which specific concepts inherit the slots defined on general concepts
C.A semantic network
D.A blackboard, on which several independent knowledge sources contribute to a shared solution

Answer: A

A decision table lists every combination of a few conditions against the action each requires, which is compact, easy to check for completeness and easy to convert into rules. Frames, networks and blackboards address structure, relations and cooperation. The table's chief virtue is that missing and contradictory combinations are visible at a glance, which is exactly what verification of a rule base struggles to achieve. Framing the question as which quantity the change acts upon makes the answer follow without calculation.

Discuss in Forum

58.

Which advantage does a decision table have over an equivalent rule set?

A.It allows the conditions to be evaluated in any order the inference engine happens to choose
B.Completeness is easy to check
C.It can express relations between objects that a set of production rules would be unable to state
D.It executes faster

Answer: B

Every combination of conditions appears as a column, so a gap or a contradiction is immediately visible, whereas the same defect hides easily in a list of rules. Expressive power is lower rather than higher, speed is comparable and evaluation order is not the point. Tables are therefore often used as the authoring medium and compiled into rules for execution.

Discuss in Forum

59.

Which term describes a set of rules that leads back to its own starting condition?

A.Redundancy
B.Unreachability, in which no combination of facts can ever satisfy a given rule's conditions
C.Circularity
D.Subsumption, in which the conditions of one rule are implied by the conditions of another

Answer: C

Circular rules can drive an engine into an unterminating loop, or at best waste effort rederiving what is already known. Refractoriness limits the damage but does not remove the defect. Subsumption, redundancy and unreachability are the other three classical faults that static verification looks for. Detecting circularity amounts to finding a cycle in the graph of rule dependencies.

Discuss in Forum

60.

Which structure does forward chaining most naturally build?

A.A proof tree rooted at the goal and branching down towards the facts the user has supplied
B.A search frontier
C.A dependency network recording which of the beliefs currently held support which other beliefs
D.A growing set of derived facts

Answer: D

Forward chaining keeps asserting conclusions into working memory until the rules are exhausted, so what accumulates is a set of derived facts. The goal-rooted proof tree belongs to backward chaining, frontiers to search and dependency networks to truth maintenance. Because everything derivable is derived, forward chaining can do a great deal of irrelevant work when only one conclusion was wanted. Reading the definition literally, rather than recalling a formula, is the reliable route through questions of this shape.

Discuss in Forum

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