Expert systems are advanced computer programs designed to mimic the decision-making abilities of human experts in specific domains. They utilize a knowledge base filled with specialized facts and rules, an inference engine to process information and draw conclusions, and a user interface for interaction. By applying logical reasoning, such as forward or backward chaining, these systems can diagnose problems, offer recommendations, and solve complex issues in fields like medicine, engineering, finance, and more. For example, a medical expert system might analyze symptoms to suggest potential diagnoses, drawing from a vast repository of expert knowledge to assist non-experts in making informed decisions. Despite their strengths in handling structured problems, expert systems require careful maintenance to keep their knowledge base current and accurate.
Table of contents
- Part 1: OnlineExamMaker AI quiz generator – Save time and efforts
- Part 2: 20 expert systems quiz questions & answers
- Part 3: Save time and energy: generate quiz questions with AI technology
Part 1: OnlineExamMaker AI quiz generator – Save time and efforts
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Part 2: 20 expert systems quiz questions & answers
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1. What is the primary component of an expert system that stores facts and rules?
a) User interface
b) Inference engine
c) Knowledge base
d) Explanation facility
Answer: c
Explanation: The knowledge base is the core repository where domain-specific knowledge is stored, allowing the system to draw upon expert-level information for decision-making.
2. In expert systems, what does forward chaining primarily involve?
a) Starting from goals and working backwards
b) Starting from data and deriving conclusions
c) Randomly searching through rules
d) Updating the user interface dynamically
Answer: b
Explanation: Forward chaining begins with available data and applies rules to reach conclusions, making it ideal for data-driven scenarios like prediction.
3. Which knowledge representation method uses IF-THEN rules?
a) Semantic networks
b) Frames
c) Production rules
d) Neural networks
Answer: c
Explanation: Production rules represent knowledge as conditional statements (IF-THEN), enabling straightforward rule-based reasoning in expert systems.
4. What is the main advantage of using an expert system over human experts?
a) It can handle emotions better
b) It provides consistent and repeatable decisions
c) It learns from mistakes faster
d) It requires less maintenance
Answer: b
Explanation: Expert systems deliver consistent outputs based on programmed rules, reducing variability that can occur with human judgment.
5. In backward chaining, the system starts from:
a) Available facts
b) Hypothetical goals
c) User inputs only
d) External databases
Answer: b
Explanation: Backward chaining works from a goal or hypothesis and seeks to verify it by tracing back through rules, commonly used in diagnostic applications.
6. Which of the following is NOT a typical component of an expert system?
a) Knowledge acquisition facility
b) Machine learning algorithm
c) Inference engine
d) Knowledge base
Answer: b
Explanation: While expert systems may integrate with machine learning, it is not a standard component; core elements include knowledge acquisition, inference, and the base.
7. What role does the inference engine play in an expert system?
a) It collects user data
b) It applies rules to the knowledge base
c) It designs the user interface
d) It updates external hardware
Answer: b
Explanation: The inference engine processes the knowledge base by applying logical rules to derive conclusions or solutions.
8. Expert systems are most effective in domains that are:
a) Highly unpredictable
b) Well-defined and static
c) Continuously evolving biologically
d) Based on creative arts
Answer: b
Explanation: They excel in domains with clear, structured knowledge, such as medical diagnostics, where rules can be precisely defined.
9. What is a common challenge in developing expert systems?
a) Overabundance of processing speed
b) Knowledge acquisition from experts
c) Excessive user creativity
d) Integration with social media
Answer: b
Explanation: Extracting and formalizing expert knowledge into a usable format is often difficult and time-consuming, known as the knowledge acquisition bottleneck.
10. Which programming language is frequently used for building expert systems?
a) C++
b) Prolog
c) JavaScript
d) Python (for general purposes)
Answer: b
Explanation: Prolog is designed for logic programming, making it suitable for rule-based systems and inference in expert systems.
11. In expert systems, what does uncertainty handling typically involve?
a) Ignoring probabilistic data
b) Using methods like certainty factors or Bayesian networks
c) Deleting uncertain rules
d) Relying solely on binary logic
Answer: b
Explanation: Uncertainty is managed through techniques like certainty factors to weigh evidence and make decisions in ambiguous situations.
12. What is the purpose of the explanation facility in an expert system?
a) To generate new rules automatically
b) To provide reasons for its conclusions
c) To encrypt the knowledge base
d) To handle user authentication
Answer: b
Explanation: It allows the system to explain its reasoning, increasing transparency and user trust in the decisions made.
13. Expert systems differ from conventional software in that they:
a) Focus on general algorithms
b) Emphasize symbolic reasoning over numerical computation
c) Require no maintenance
d) Operate without rules
Answer: b
Explanation: They prioritize symbolic manipulation of knowledge, such as rules and logic, rather than pure numerical processing.
14. Which application is a classic example of an expert system?
a) MYCIN
b) A basic calculator app
c) A video game engine
d) A cloud storage system
Answer: a
Explanation: MYCIN was an early expert system for diagnosing bacterial infections, demonstrating rule-based medical advice.
15. What happens during the knowledge engineering phase?
a) Testing the system’s speed
b) Eliciting and structuring expert knowledge
c) Deploying the system globally
d) Deleting outdated data
Answer: b
Explanation: Knowledge engineering involves gathering and organizing domain expertise into a format suitable for the expert system.
16. In expert systems, frames are used for:
a) Storing numerical data only
b) Representing objects with slots for attributes
c) Executing backward chaining exclusively
d) Handling user interfaces
Answer: b
Explanation: Frames organize knowledge by defining objects and their properties, facilitating object-oriented reasoning.
17. Why might an expert system fail in real-world use?
a) Due to perfect accuracy
b) Incomplete knowledge or inability to adapt to new data
c) Excessive speed in processing
d) Lack of user interest
Answer: b
Explanation: If the knowledge base is not comprehensive or the system can’t handle unforeseen scenarios, performance can degrade.
18. What is hybrid expert systems?
a) Systems that only use rules
b) Combinations of expert systems with other AI techniques like neural networks
c) Systems designed for a single user
d) Basic software without AI
Answer: b
Explanation: Hybrid systems integrate expert systems with other methods to enhance capabilities, such as combining rules with machine learning.
19. The blackboard architecture in expert systems is used for:
a) Solving problems with multiple knowledge sources
b) Storing simple data
c) Running inference engines sequentially
d) Limiting user access
Answer: a
Explanation: It allows different knowledge modules to collaborate on a shared “blackboard,” ideal for complex, multi-expert problems.
20. What is the key benefit of validating an expert system?
a) To increase its aesthetic appeal
b) To ensure accuracy and reliability in decision-making
c) To reduce the number of rules
d) To make it faster for non-experts
Answer: b
Explanation: Validation confirms that the system produces correct outputs, crucial for applications like financial or medical advice.
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Part 3: Save time and energy: generate quiz questions with AI technology
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