Тест: Expert System Inference Techniques — 9 въпроса

1. What is the primary function of system experts in knowledge-based systems?

A. To generate random solutions for problems

B. To replace human decision-makers entirely

C. To store data without processing it

D. To simulate human decision-making using rules and facts

Answer: (D)

Обяснение

System experts are designed to simulate human decision-making processes by using rules and facts to solve problems, such as diagnosis or planning, within knowledge-based systems.

2. What are the core components of an expert system as outlined in the revision sheet?

A. Rules base, facts base, inference engine, user interface

B. Knowledge database, reasoning processor, user panel, data store

C. Inference rules, data facts, graphical interface, communication module

D. Knowledge engine, facts repository, decision maker, display unit

Answer: (A)

Обяснение

The core components are the rules base, facts base, inference engine, and user interface, as these facilitate knowledge storage, reasoning, and user interaction.

3. Which inference method involves reasoning from goals to data in a knowledge-based system?

A. Forward chaining

B. Graph traversal

C. Mixed chaining

D. Backward chaining

Answer: (D)

Обяснение

Backward chaining reasoning starts from a goal or hypothesis and works backward to find supporting facts, making it goal-driven, unlike forward chaining which starts from data.

4. Which inference method is described as data-driven, starting from facts and moving towards conclusions?

A. Backward chaining

B. Forward chaining

C. Mixed chaining

D. Hierarchical inference

Answer: (B)

Обяснение

Forward chaining is data-driven, beginning with known facts and applying rules to derive new conclusions, unlike backward chaining which is goal-driven.

5. What is considered a major bottleneck in developing expert systems according to the summary?

A. Collecting user feedback

B. Designing the user interface

C. Implementing hardware solutions

D. Writing rules and representing problems accurately

Answer: (D)

Обяснение

The summary highlights that rule creation and problem representation are critical bottlenecks because they directly affect the system's reasoning efficiency and accuracy.

6. According to the revision sheet, what is a major bottleneck in expert system performance?

A. The inference engine's speed

B. Modeling the problem and writing rules

C. User interface design

D. Fact storage capacity

Answer: (B)

Обяснение

Problem modeling and rule writing are major performance bottlenecks because they directly impact how efficiently the system can reason and infer.

7. Which statement correctly describes the purpose of uncertainty modeling in expert systems?

A. To eliminate the need for rules

B. To handle incomplete or uncertain data via probabilistic rules and facts

C. To simplify the rules base by reducing probabilities

D. To convert all facts into certain conclusions

Answer: (B)

Обяснение

Uncertainty modeling introduces probabilities to rules and facts, allowing systems to handle incomplete or uncertain information realistically.

8. What is the primary difference between forward chaining and backward chaining?

A. Forward chaining is goal-driven, backward is data-driven

B. Forward chaining starts from facts, backward from goals

C. They are two names for the same process

D. Backward chaining uses probabilities, forward does not

Answer: (B)

Обяснение

Forward chaining is data-driven, starting from facts, while backward chaining is goal-driven, starting from a goal and searching for supporting facts.

9. Based on the revision sheet, what is the significance of representing problems accurately in expert systems?

A. It enhances the system’s ability to simulate human reasoning

B. It is necessary for effective reasoning and inference efficiency

C. It ensures all rules are valid

D. It replaces the need for an inference engine

Answer: (B)

Обяснение

Accurate problem representation is critical because it defines the constraints and initial data, directly impacting the efficiency and effectiveness of the inference process.