100 Most Important MCQs for Bihar STET Unit 13: Artificial Intelligence
Introduction to AI and Production Systems
1. What is the primary goal of Artificial Intelligence (AI)?
a) Automating manual tasks
b) Simulating human intelligence in machines
c) Reducing computational power
d) Limiting data processing
Answer: b
Explanation: AI aims to create systems that mimic human intelligence, including reasoning, learning, and problem-solving.
2. What is problem formulation in AI?
a) Writing code for AI systems
b) Defining the problem in terms of states, actions, and goals
c) Designing hardware for AI
d) Collecting raw data
Answer: b
Explanation: Problem formulation involves defining the initial state, goal state, and possible actions to solve a problem.
3. Which of the following is a characteristic of a production system?
a) Static rules
b) A set of rules, a database, and a control strategy
c) Limited memory
d) No goal state
Answer: b
Explanation: A production system consists of rules, a database of facts, and a control strategy to apply rules.
4.What is a control strategy in production systems?
a) Forward chaining
b) Backward chaining
c) Depth-first search
d) Breadth-first search
Answer: a
Explanation: Forward chaining applies rules in a sequence to derive conclusions, guiding the production system.
5. Which search strategy explores all nodes at the current depth before moving deeper?
a) Depth-first search
b) Breadth-first search
c) Hill climbing
d) Constraint satisfaction
Answer: b
Explanation: Breadth-first search explores all nodes at the current level before moving to the next level.
6. What is a key characteristic of AI problems?
a) Fully observable environment
b) Static and predictable outcomes
c) Simple and linear solutions
d) Single-step solutions
Answer: a
Explanation: AI problems often assume a fully observable environment to define states and actions clearly.
7. What is a heuristic function used for in AI?
a) To store data
b) To estimate the cost to reach the goal state
c) To encrypt information
d) To reduce memory usage
Answer: b
8. Explanation: Heuristic functions provide an estimated cost to guide search algorithms efficiently.
Which algorithm is a local search method that moves toward better solutions?
a) Depth-first search
b) Breadth-first search
c) Hill climbing
d) Constraint satisfaction
Answer: c
Explanation: Hill climbing selects the best neighboring state to approach the goal.
9. What is the purpose of indexing in AI problem-solving?
a) To encrypt data
b) To speed up retrieval of relevant rules or knowledge
c) To limit search space
d) To define goal states
Answer: b
Explanation: Indexing organizes knowledge to enable faster access during problem-solving.
10. What measures the performance of search algorithms?
a) Memory usage
b) Time complexity, space complexity, and solution quality
c) Data encryption level
d) Hardware efficiency
Answer: b
Explanation: Performance is evaluated based on time, space, and the quality of the solution found.
11. Which method is used to solve constraint satisfaction problems?
a) Hill climbing
b) Backtracking
c) Breadth-first search
d) Depth-first search
Answer: b
Explanation: Backtracking systematically explores possible solutions for constraint satisfaction problems.
12. What is a specialized production system?
a) A system with no rules
b) A system tailored for specific problem domains
c) A system with unlimited memory
d) A system with no control strategy
Answer: b
Explanation: Specialized systems are designed for specific tasks, like expert systems.
13. Which search strategy may get stuck in local optima?
a) Breadth-first search
b) Depth-first search
c) Hill climbing
d) Constraint satisfaction
Answer: c
Explanation: Hill climbing can get trapped in local optima, missing the global optimum.
14. What is a problem graph in AI?
a) A visual styling tool
b) A representation of states and actions as nodes and edges
c) A hardware diagram
d) A data storage model
Answer: b
Explanation: Problem graphs represent states as nodes and actions as edges for search algorithms.
15. What is the role of matching in production systems?
a) Encrypting data
b) Comparing conditions of rules with the current state
c) Reducing memory usage
d) Defining hardware requirements
Answer: b
Explanation: Matching checks if rule conditions align with the current state.
Representation of Knowledge
16. What is a key feature of knowledge representation in AI?
a) Limiting data access
b) Structuring information for reasoning and problem-solving
c) Encrypting data
d) Reducing computational power
Answer: b
Explanation: Knowledge representation organizes information to enable reasoning.
17. Which logic is used for knowledge representation in AI?
a) Binary logic
b) Predicate logic
c) Fuzzy logic
d) Boolean logic
Answer: b
Explanation: Predicate logic represents knowledge using predicates and quantifiers.
18. What is predicate calculus?
a) A hardware design method
b) A formal system for reasoning with predicates
c) A data encryption technique
d) A memory management tool
Answer: b
Explanation: Predicate calculus formalizes reasoning with predicates and logical connectives.
19. What is resolution in predicate calculus?
a) A search algorithm
b) A method to prove theorems by contradiction
c) A data storage technique
d) A heuristic function
Answer: b
Explanation: Resolution proves theorems by deriving contradictions from negated goals.
20. Which logic is NOT typically used for knowledge representation?
a) Predicate logic
b) Fuzzy logic
c) Modal logic
d) Assembly logic
Answer: d
Explanation: Assembly logic is not a standard AI knowledge representation method.
21. What is a structured representation of knowledge?
a) Random data storage
b) Organized frameworks like frames or semantic networks
c) Unstructured text
d) Encrypted data
Answer: b
Explanation: Structured representations like frames organize knowledge hierarchically.
22. What is a key benefit of predicate logic?
a) Limited expressiveness
b) Precise and formal representation of knowledge
c) High memory usage
d) Manual processing
Answer: b
Explanation: Predicate logic provides a precise way to represent complex knowledge.
23. Which is an example of a game-playing AI problem?
a) Chess
b) Data encryption
c) Hardware design
d) Web styling
Answer: a
Explanation: Chess is a classic AI problem involving strategic decision-making.
24. What is a semantic network?
a) A network protocol
b) A graph-based knowledge representation
c) A hardware component
d) A search algorithm
Answer: b
Explanation: Semantic networks represent knowledge as nodes (concepts) and edges (relationships).
What is a frame in knowledge representation?
a) A search strategy
b) A structured template for organizing knowledge
c) A data encryption method
d) A hardware component
Answer: b
Explanation: Frames are templates that organize related knowledge attributes.
Knowledge Inference
What is inference in AI?
a) Data storage
b) Deriving new knowledge from existing knowledge
c) Hardware design
d) Reducing memory usage
Answer: b
Explanation: Inference derives new facts or conclusions using existing knowledge.
What is backward chaining?
a) Starting from the goal and working backward
b) Starting from initial facts and moving forward
c) A search algorithm
d) A heuristic function
Answer: a
Explanation: Backward chaining starts with the goal and works backward to find supporting facts.
What is forward chaining?
a) Starting from the goal
b) Starting from known facts and deriving conclusions
c) A constraint satisfaction method
d) A local search algorithm
Answer: b
Explanation: Forward chaining applies rules to known facts to derive new conclusions.
What is the rule value approach in inference?
a) Encrypting rules
b) Assigning weights to rules for decision-making
c) Reducing rule usage
d) Manual rule application
Answer: b
Explanation: The rule value approach assigns weights to rules to prioritize them.
What is fuzzy reasoning used for?
a) Precise logic
b) Handling uncertainty and partial truths
c) Hardware design
d) Data encryption
Answer: b
Explanation: Fuzzy reasoning deals with imprecise or uncertain information.
What are certainty factors in AI?
a) Hardware specifications
b) Measures of belief in uncertain knowledge
c) Search algorithms
d) Data storage methods
Answer: b
Explanation: Certainty factors quantify confidence in uncertain inferences.
What is the Bayesian theory used for in AI?
a) Hardware design
b) Probabilistic reasoning under uncertainty
c) Data encryption
d) Reducing memory usage
Answer: b
Explanation: Bayesian theory uses probabilities to reason under uncertainty.
What is a Bayesian Network?
a) A network protocol
b) A graphical model for probabilistic relationships
c) A search algorithm
d) A hardware component
Answer: b
Explanation: Bayesian Networks represent dependencies among variables probabilistically.
What is the Dempster-Shafer theory used for?
a) Data encryption
b) Combining evidence with uncertainty
c) Hardware design
d) Search optimization
Answer: b
Explanation: Dempster-Shafer theory combines evidence to handle uncertainty.
Which inference method is goal-driven?
a) Forward chaining
b) Backward chaining
c) Hill climbing
d) Breadth-first search
Answer: b
Explanation: Backward chaining starts with the goal and works backward.
Planning and Machine Learning
What is a basic plan generation system in AI?
a) A hardware design tool
b) A system to create action sequences to achieve goals
c) A data encryption method
d) A memory management tool
Answer: b
Explanation: Plan generation systems create sequences of actions to reach goals.
What is STRIPS in AI planning?
a) A search algorithm
b) A planning system using preconditions and effects
c) A heuristic function
d) A data storage method
Answer: b
Explanation: STRIPS (Stanford Research Institute Problem Solver) uses preconditions and effects for planning.
What is K-STRIPS?
a) A basic planning system
b) An advanced planning system with knowledge integration
c) A search algorithm
d) A hardware component
Answer: b
Explanation: K-STRIPS extends STRIPS with advanced knowledge-based planning.
What is a strategic explanation in AI?
a) Explaining hardware design
b) Justifying the reasoning behind a plan or decision
c) Encrypting data
d) Reducing memory usage
Answer: b
Explanation: Strategic explanations justify the logic behind AI decisions.
What is a "Why" explanation in AI?
a) Explaining hardware requirements
b) Explaining the reason for a decision or action
c) A search algorithm
d) A data storage method
Answer: b
Explanation: "Why" explanations clarify the rationale for AI decisions.
What is machine learning in AI?
a) Manual data processing
b) Enabling systems to learn from data without explicit programming
c) Hardware design
d) Data encryption
Answer: b
Explanation: Machine learning enables systems to improve from experience.
What is adaptive learning in AI?
a) Static rule-based learning
b) Learning that adjusts based on new data
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: Adaptive learning updates models based on new information.
Which is a type of machine learning?
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
d) All of the above
Answer: d
Explanation: Machine learning includes supervised, unsupervised, and reinforcement learning.
What is supervised learning?
a) Learning without labeled data
b) Learning with labeled input-output pairs
c) Learning by trial and error
d) Manual rule definition
Answer: b
Explanation: Supervised learning uses labeled data to train models.
What is unsupervised learning?
a) Learning with labeled data
b) Learning patterns from unlabeled data
c) Learning by rewards
d) Manual data processing
Answer: b
Explanation: Unsupervised learning finds patterns in unlabeled data.
Expert Systems
What is an expert system?
a) A hardware design tool
b) A system that emulates human expert decision-making
c) A search algorithm
d) A data encryption method
Answer: b
Explanation: Expert systems mimic human expertise in specific domains.
What is a key component of an expert 시스템?
a) Knowledge base
b) Network protocol
c) Hardware component
d) Search algorithm
Answer: a
Explanation: The knowledge base stores domain-specific knowledge in expert systems.
What is the role of the inference engine in expert systems?
a) Encrypting data
b) Applying rules to derive conclusions
c) Designing hardware
d) Reducing memory usage
Answer: b
Explanation: The inference engine applies rules to the knowledge base for reasoning.
What is knowledge acquisition in expert systems?
a) Hardware optimization
b) Gathering knowledge from experts or sources
c) Data encryption
d) Search optimization
Answer: b
Explanation: Knowledge acquisition involves collecting expert knowledge for the system.
What is meta-knowledge in expert systems?
a) Knowledge about hardware
b) Knowledge about the system’s own knowledge and reasoning
c) Encrypted knowledge
d) Limited knowledge
Answer: b
Explanation: Meta-knowledge is knowledge about how the system operates.
What is a heuristic in expert systems?
a) A hardware component
b) A rule of thumb to guide decision-making
c) A search algorithm
d) A data storage method
Answer: b
Explanation: Heuristics are practical rules to simplify complex decisions.
Which is an example of an expert system?
a) MYCIN
b) HTML
c) CSS
d) JavaScript
Answer: a
Explanation: MYCIN is a classic expert system for medical diagnosis.
What is the purpose of DART?
a) Web styling
b) Diagnosing computer hardware faults
c) Data encryption
d) Search optimization
Answer: b
Explanation: DART diagnoses faults in computer hardware.
What is XOON?
a) A network protocol
b) An expert system for oil exploration
c) A search algorithm
d) A hardware component
Answer: b
Explanation: XOON is an expert system for oil exploration tasks.
What is an expert system shell?
a) A hardware casing
b) A framework for building expert systems
c) A search algorithm
d) A data encryption tool
Answer: b
Explanation: Expert system shells provide tools to create domain-specific systems.
Additional MCQs for Comprehensive Coverage
What is the primary limitation of depth-first search?
a) High memory usage
b) Potential to get stuck in infinite loops
c) Slow computation
d) Limited scalability
Answer: b
Explanation: Depth-first search may explore infinite paths without finding the goal.
What is a key feature of breadth-first search?
a) Low memory usage
b) Guaranteed optimal solutions
c) Fast computation
d) Limited exploration
Answer: b
Explanation: Breadth-first search finds the shortest path to the goal.
What is a constraint in constraint satisfaction problems?
a) A hardware limitation
b) A condition that must be satisfied
c) A search algorithm
d) A data encryption method
Answer: b
Explanation: Constraints define conditions that solutions must meet.
What is the role of a heuristic function in hill climbing?
a) Encrypting data
b) Guiding the search toward better states
c) Reducing memory usage
d) Designing hardware
Answer: b
Explanation: Heuristics guide hill climbing toward promising solutions.
What is a key challenge in AI problem-solving?
a) Limited data
b) Computational complexity
c) Excessive memory
d) Simple solutions
Answer: b
Explanation: Many AI problems are computationally complex.
Which logic uses truth values between 0 and 1?
a) Predicate logic
b) Fuzzy logic
c) Modal logic
d) Boolean logic
Answer: b
Explanation: Fuzzy logic handles partial truths with values between 0 and 1.
What is a key feature of MYCIN?
a) Web styling
b) Diagnosing bacterial infections
c) Hardware design
d) Data encryption
Answer: b
Explanation: MYCIN is an expert system for diagnosing infections.
What is a production rule in AI?
a) A hardware specification
b) An if-then statement for reasoning
c) A search algorithm
d) A data storage method
Answer: b
Explanation: Production rules are condition-action pairs for reasoning.
What is the purpose of a "How" explanation in AI?
a) Explaining hardware design
b) Describing the process of reaching a decision
c) Encrypting data
d) Reducing memory usage
Answer: b
Explanation: "How" explanations detail the steps in decision-making.
What is reinforcement learning?
a) Learning with labeled data
b) Learning through rewards and penalties
c) Learning without data
d) Manual rule definition
Answer: b
Explanation: Reinforcement learning uses rewards to guide learning.
What is a key feature of expert systems?
a) Limited knowledge
b) Domain-specific expertise
c) General-purpose computing
d) Data encryption
Answer: b
Explanation: Expert systems focus on specific domains.
What is a frame-based system?
a) A search algorithm
b) A structured knowledge representation system
c) A hardware component
d) A data encryption method
Answer: b
Explanation: Frame-based systems organize knowledge using templates.
What is the primary role of the knowledge base in expert systems?
a) Encrypting data
b) Storing domain-specific knowledge
c) Designing hardware
d) Reducing memory usage
Answer: b
Explanation: The knowledge base stores facts and rules for reasoning.
What is a key challenge in knowledge acquisition?
a) Excessive data
b) Extracting accurate knowledge from experts
c) Limited memory
d) Simple rules
Answer: b
Explanation: Acquiring accurate knowledge is challenging due to expert articulation issues.
What is a "Why not" explanation in AI?
a) Explaining hardware limitations
b) Explaining why a decision was not made
c) Encrypting data
d) Reducing memory usage
Answer: b
Explanation: "Why not" explanations clarify why alternative decisions were rejected.
What is the primary goal of STRIPS?
a) Data encryption
b) Generating action plans to achieve goals
c) Hardware design
d) Search optimization
Answer: b
Explanation: STRIPS generates plans using preconditions and effects.
What is a key feature of Bayesian Networks?
a) Manual data processing
b) Representing probabilistic dependencies
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: Bayesian Networks model probabilistic relationships.
What is the purpose of fuzzy reasoning?
a) Precise logic
b) Handling vague or uncertain information
c) Hardware design
d) Data storage
Answer: b
Explanation: Fuzzy reasoning manages uncertainty with partial truths.
What is a key component of an expert system architecture?
a) User interface
b) Network protocol
c) Search algorithm
d) Data encryption
Answer: a
Explanation: The user interface allows interaction with the expert system.
What is the primary limitation of hill climbing?
a) High memory usage
b) Getting stuck in local optima
c) Slow computation
d) Limited scalability
Answer: b
Explanation: Hill climbing may miss the global optimum.
What is a key feature of predicate calculus?
a) Limited expressiveness
b) Formal representation with quantifiers
c) Hardware design
d) Data encryption
Answer: b
Explanation: Predicate calculus uses quantifiers for formal reasoning.
What is the role of the control strategy in production systems?
a) Encrypting data
b) Determining the order of rule application
c) Designing hardware
d) Reducing memory usage
Answer: b
** printed) Depth-first search
b) Breadth-first search
c) Hill climbing
d) Constraint satisfaction
Answer: a
Explanation: The control strategy decides which rules to apply in a production system.
What is a key benefit of machine learning?
a) Manual data processing
b) Automatic pattern recognition
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: Machine learning identifies patterns without explicit programming.
What is the primary role of MYCIN?
a) Web styling
b) Diagnosing bacterial infections
c) Hardware design
d) Data encryption
Answer: b
Explanation: MYCIN diagnoses bacterial infections using expert knowledge.
What is a key feature of adaptive learning?
a) Static models
b) Continuous model updates with new data
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: Adaptive learning refines models based on new data.
What is the purpose of resolution in AI?
a) Data storage
b) Proving theorems by contradiction
c) Hardware design
d) Reducing memory usage
Answer: b
Explanation: Resolution proves logical statements by deriving contradictions.
What is a semantic network used for?
a) Network protocols
b) Representing knowledge as graphs
c) Hardware design
d) Search optimization
Answer: b
Explanation: Semantic networks represent knowledge using nodes and edges.
What is a key challenge in expert systems?
a) Excessive data
b) Knowledge acquisition bottlenecks
c) Limited memory
d) Simple rules
Answer: b
Explanation: Acquiring accurate expert knowledge is a key challenge.
What is the role of the inference engine?
a) Encrypting data
b) Applying rules to derive conclusions
c) Designing hardware
d) Reducing memory usage
Answer: b
Explanation: The inference engine reasons using the knowledge base.
What is a key feature of STRIPS planning?
a) Manual planning
b) Using preconditions and effects for planning
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: STRIPS plans using action preconditions and effects.
What is a key benefit of Bayesian theory?
a) Hardware design
b) Probabilistic reasoning under uncertainty
c) Data encryption
d) Reducing memory usage
Answer: b
Explanation: Bayesian theory handles uncertainty probabilistically.
What is a frame-based system?
a) A search algorithm
b) A structured knowledge representation system
c) A hardware component
d) A data encryption method
Answer: b
Explanation: Frame-based systems use templates for knowledge representation.
What is a key feature of reinforcement learning?
a) Labeled data
b) Learning through rewards
c) Manual rule definition
d) Data encryption
Answer: b
Explanation: Reinforcement learning uses rewards to guide learning.
What is the role of meta-knowledge?
a) Hardware optimization
b) Knowledge about the system’s reasoning process
c) Data encryption
d) Search optimization
Answer: b
Explanation: Meta-knowledge describes the system’s own reasoning.
What is a key feature of fuzzy logic?
a) Binary reasoning
b) Handling partial truths
c) Hardware design
d) Data storage
Answer: b
Explanation: Fuzzy logic manages partial truths and uncertainty.
What is the primary role of DART?
a) Web styling
b) Diagnosing computer hardware faults
c) Data encryption
d) Search optimization
Answer: b
Explanation: DART diagnoses hardware faults in computers.
What is a key feature of expert system shells?
a) Hardware casing
b) Frameworks for building expert systems
c) Search algorithms
d) Data encryption
Answer: b
Explanation: Expert system shells provide tools for creating systems.
What is the primary limitation of breadth-first search?
a) Low memory usage
b) High memory usage
c) Slow computation
d) Limited scalability
Answer: b
Explanation: Breadth-first search requires significant memory for large graphs.
What is a key feature of constraint satisfaction problems?
a) Hardware limitations
b) Satisfying multiple constraints
c) Search algorithms
d) Data encryption
Answer: b
Explanation: Constraint satisfaction problems involve satisfying multiple conditions.
What is the role of heuristics in expert systems?
a) Encrypting data
b) Guiding decision-making with practical rules
c) Designing hardware
d) Reducing memory usage
Answer: b
Explanation: Heuristics provide practical rules for decision-making.
What is a key feature of K-STRIPS?
a) Basic planning
b) Knowledge-integrated planning
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: K-STRIPS enhances STRIPS with knowledge integration.
What is the primary role of the user interface in expert systems?
a) Encrypting data
b) Facilitating user interaction
c) Designing hardware
d) Reducing memory usage
Answer: b
Explanation: The user interface enables interaction with the system.
What is a key benefit of supervised learning?
a) Manual data processing
b) Accurate predictions with labeled data
c) Hardware optimization
d) Data encryption
Answer: b
Explanation: Supervised learning uses labeled data for accurate predictions.
What is a key feature of the Dempster-Shafer theory?
a) Hardware design
b) Combining evidence with uncertainty
c) Data encryption
d) Search optimization
Answer: b
Explanation: Dempster-Shafer theory handles uncertainty by combining evidence.
What is the primary role of XOON?
a) Web styling
b) Supporting oil exploration decisions
c) Data encryption
d) Hardware design
Answer: b
Explanation: XOON is an expert system for oil exploration tasks.