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Artificial Intelligence MCQ – Agents

Here are 25 multiple-choice questions (MCQs) related to Artificial Intelligence focusing on the topic of Agents. Each question includes four options, the correct answer, and a brief explanation. Go ahead and test your knowledge of AI Agents with these 25 multiple-choice questions (MCQs).

1. What is the primary function of an intelligent agent?

Explanation:.

An intelligent agent is designed to perform tasks and make decisions autonomously on behalf of a user or another system.

2. Which of the following is a key characteristic of an intelligent agent?

Autonomy is a fundamental characteristic of intelligent agents, allowing them to operate independently without direct human intervention.

3. In AI, the term "rational agent" refers to an agent that:

A rational agent is one that acts to achieve the best possible outcome or, when there is uncertainty, the best expected outcome.

4. The environment in which an AI agent operates can be:

AI agents can operate in physical environments (like robots) or virtual environments (like software agents).

5. A key distinction between a simple reflex agent and a model-based reflex agent is:

Unlike simple reflex agents, model-based reflex agents can maintain an internal state to keep track of the world, allowing them to handle partially observable environments.

6. Which type of AI agent is most suitable for dynamic and unpredictable environments?

Utility-based agents can evaluate different states according to a utility function, making them well-suited for dynamic and unpredictable environments.

7. Learning agents in AI are characterized by their ability to:

Learning agents are capable of improving their performance over time through experience, adapting to new circumstances and environments.

8. In AI, the term "percepts" refers to:

Percepts are the pieces of information an agent receives from its environment, typically through sensors.

9. What role does a "utility function" play in AI agents?

A utility function is used by an agent to evaluate and rank different states based on their desirability or utility.

10. Which of the following best defines a "goal-based" agent in AI?

Goal-based agents are designed to perform actions that lead towards achieving specific predefined goals.

11. The concept of "bounded rationality" in AI refers to:

Bounded rationality acknowledges that an agent's decision-making is limited by factors like incomplete information and finite computational resources.

12. An AI agent that uses past experiences to inform current decisions is utilizing:

Reinforcement learning involves an agent learning from the consequences of its actions and adjusting its strategy based on past experiences.

13. The "PEAS" description of an AI agent includes:

PEAS stands for Performance measure, Environment, Actions, and Sensors, which are key elements in describing and designing an AI agent.

14. In AI, an agent's "sensors" are used to:

Sensors are the tools through which an agent perceives its environment, gathering data that it uses to make decisions.

15. Which AI concept involves an agent improving its performance by observing and mimicking human behavior?

Cognitive modeling involves creating AI agents that can learn and improve by observing and imitating human behavior and thought processes.

16. An agent that can handle new and unforeseen scenarios through generalization is using:

Machine learning enables an agent to generalize from past experiences and handle new, unforeseen scenarios effectively.

17. The "actuators" of an AI agent are responsible for:

Actuators are the components that enable an AI agent to perform actions in its environment, such as moving or manipulating objects.

18. An AI agent designed for a "fully observable environment":

In a fully observable environment, the agent can access the complete state of the environment at any time, aiding in decision-making.

19. The main difference between a deterministic environment and a stochastic environment in AI is:

In a deterministic environment, the outcome of actions is predictable, whereas in a stochastic environment, there is some level of unpredictability or randomness.

20. A "discrete" environment in the context of AI agents refers to:

In a discrete environment, there are a finite number of distinct and clearly defined states, as opposed to a continuous environment where there are infinite possible states.

21. The term "agent program" in AI refers to:

The agent program is the software component that dictates the agent's decision-making process and behavior based on its perceptions.

22. In the context of AI, a "static" environment is one where:

In a static environment, the state of the environment does not change while the agent is making a decision.

23. An AI agent's "performance measure" is used to:

The performance measure is a criterion used to evaluate how well an agent is performing its tasks and achieving its objectives.

24. The "frame problem" in AI is primarily concerned with:

The frame problem in AI deals with the challenge of representing and reasoning about the effects of actions, especially considering what changes and what remains the same in a dynamic environment.

25. In AI, the term "task environment" refers to:

The task environment in AI refers to the specific tasks, problems, or goals that an AI agent is designed to solve or achieve, including the challenges and constraints it might face.

Related MCQ (Multiple Choice Questions) :

Artificial intelligence mcq – natural language processing, artificial intelligence mcq – problem-solving agents, artificial intelligence mcq – partial order planning, artificial intelligence mcq – expert systems, artificial intelligence mcq – fuzzy logic, artificial intelligence mcq – neural networks, artificial intelligence mcq – robotics, artificial intelligence mcq – rule-based system, artificial intelligence mcq – semantic networks, artificial intelligence mcq – bayesian networks, artificial intelligence mcq – alpha beta pruning, artificial intelligence mcq – text mining, leave a comment cancel reply.

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Home » MCQs

• Artificial Intelligence MCQs

Artificial Intelligence MCQs : This section contains multiple-choice questions on Artificial Intelligence . All MCQs have the correct answers and explanations. These MCQs will help students and professionals to test their skills and to enhance their knowledge of Artificial Intelligence .

List of Artificial Intelligence MCQs

1. Which of the following are comprised within AI?

• Machine Learning
• Deep Learning
• Both (A) and (B)
• None of the above

Answer: C) Both (A) and (B)

Explanation:

Both Machine Learning and Deep Learning are the sub-categories of Artificial Intelligence . They are studied differently due to their depth of subject and vast areas of application.

Discuss this Question

2. State whether the following condition is true or false? "Artificial Intelligence means to mimic a human. Hence, if a robot can move from one place to another like a human, then it comes under Artificial Intelligence."

Answer: B) False

AI deals with human behavior rather than human actions. It deals with the way the human mind thinks and causes the body to act in a way in any particular situation. Hence, if a robot just moves like humans through external commands, we cannot say that AI has been implemented for moving that robot.

3. Which of the mentioned human behavior does the AI aim to mimic?

Answer: A) Thinking

The main and foremost aim of Artificial Intelligence is to make the machine think and act like humans.

4. Which of the following is not a goal of AI?

• Thinking humanly
• Adapting to the environment and situations
• To rule over humans
• Real Life Problem Solving

Answer: C) To rule over humans

The goal of AI might be to mimic human behavior and its way of thinking, but its goal is never to make the AI robots rule on humans. If this would happen, it would lead to a serious man-made disaster.

5. "In AI, we study the whole universe by dividing it into two components." What are these two components?

• Sky and Land
• Agent and environment

Answer: B) Agent and environment

In AI, we study the whole universe by dividing it into two components: Agent and Environment. The agent is the system under study and all its surroundings are termed as the environment.

6. Which of the following are the main tasks of an AI agent?

• Movement and Humanly Actions
• Perceiving and acting on the environment
• Input and Output

Answer: B) Perceiving and acting on the environment

The main task of an AI-based agent is to perceive the relevant information and data from its environment and act upon it accordingly.

7. State whether the following condition is true or false? "An Artificial Intelligence-based agent does not require capable of doing tasks on its own without any human intervention for inputs or other commands."

Answer: A) True

An AI-based agent is able to perform the entire tasks on its own. Provided, the agent is following strong AI and not weak AI.

8. Which of the mentioned parts of an agent would you consider to be the most valuable in terms of AI?

• Sensors and Actuators
• Wheels and steering
• Arms and legs
• All of the above

Answer: A) Sensors and Actuators

The sensors and actuators are the most important parts of an AI-based agent. This is because, even if an agent is not doing some manual tasks, still it needs sensors and actuators for perceiving and acting upon the environment.

9. Which of the following is not a goal of an AI agent?

• Perceiving data from the environment
• Acting upon the Environment
• Reversing the previously performed actions

Answer: D) Reversing the previously performed actions

The goal of the AI agent cannot be reversing the previously performed actions because this thing is not possible. If something has been taken place, and it is an irreversible process, then no agent can reverse it to its previous state.

10. "An AI agent is defined though it's PEAS." What does the term PEAS stand for?

• Personal Enhancement Area in Science
• Performance, Environment, Actuators and Sensors
• Performance, Entity, Area, State

Answer: B) Performance, Environment, Actuators and Sensors

Any AI agent is defined through its four factors: Performance, Environment, Actuators and Sensors.

11. Which of the following is a valid AI agent type?

• Simple based Reflex agent
• Model Based Reflex Agent
• Goal Based Agent

Answer: D) All of the above

All the mentioned agents are valid types of AI agents. The simple based reflex agent works only on the current problem and does not consider anything else. The model-based reflex agent works similarly but can also work in a partially observable environment. And the goal-based agent works to meet the goal as soon as possible.

12. State whether the following condition is true or false? "A simple reflex based agent does not care about meeting the utility of the user."

The simple based reflex agent is designed only to respond to the currently occurring problem. I do not bother about the effect its actions will cause on the environments or the utility of the user.

13. Which of the mentioned properties of the Utility-based AI agent differentiates it from the rest of the AI agents?

• Responding and providing solution to the problem
• Meeting the preference of the user
• Meeting the goal

Answer: B) Meeting the preference of the user

The utility-based agent focuses more on the utilities and preferences of the user to satisfy the user's needs. This property of this agent differentiates it from the other types of AI agents.

14. Which of the following does not represent a Goal based agent?

• Reaching the goal in minimal amount of time
• Reaching the goal in minimal cost
• Reaching the initial state again after reaching the goal state

Answer: C) Reaching the initial state again after reaching the goal state

The goal-based agent focuses only on reaching the goal state. Going back to the initial state is not the necessary action for it to take unless necessary.

15. Which of the following is considered as the most powerful AI agent?

• Simple based reflex agent
• Model based reflex agent
• Goal based agent
• Utility based agent

Answer: D) Utility based agent

The utility-based agent is termed as the most powerful agent because it meets all the user needs as well as takes care whether the user is satisfied or not.

16. Which of the following classifications of the environment are valid?

• Deterministic and Non-Deterministic
• Observable and partially-observable
• Static and dynamic

All the mentioned classifications of the environment are valid. The environment for an AI-based system can be classified using any of the mentioned ways. There are two more ways of classification of environments that are not listed. They are: Accessible and Inaccessible; and continuous and Discrete.

17. State whether the following condition is true or false? "The classification of the environment is independent of the type of AI model being used."

The classification of the environment is done only so that it becomes easier for the AI agent to perceive it. In the real, no such classification exists because the environment is vast and more likely unpredictable. Therefore, this classification highly depends upon the type of Agent.

18. Which of the mentioned Environment Classifications determine whether the environment variables are constant or keep changing?

• Deterministic and non- Deterministic

Answer: C) Static and dynamic

The classification of the environment which classifies the environment into two categories: Static and dynamic determines whether the environment variables are static or dynamic. If you want to deal with a continuous or static environment variable, then this type of classification must be chosen.

19. Which of the following does not represent a valid environment type according to AI classification of environments?

• Left sided and right sided

Answer: D) Left sided and right sided

There is no classification of the environment such as left-sided or right-sided. Thus this classification is invalid.

20. Which of the following is considered as the most specific environment classifications in AI?

• Discrete or Continuous

Answer: D) None of the above

None of the classifications can be termed as the most specific. Every classification is important and it solely depends on the type of situation and agent that which classification should be considered.

21. Which of the following definitions correctly defines the State-space in an AI system?

• A state space can be defined as the collection of all the problem states
• A state space is a state which exists in environment which is in outer space
• A state space is the total space available for the agent in the state

Answer: A) A state space can be defined as the collection of all the problem states

All the possible states for an AI system together form the state space. The state-space means the collection of all those states in which the agent can be.

22. State whether the following condition is true or false? "An AI agent cannot be in any other state except for those included in the state space for that particular system."

An AI system can be only in a state that is defined in its state space. This is because the state space is the collection of all the possible states that the system can be in. Hence, there exists no other state except for these in which the system can reside.

23. Which of the mentioned definitions correctly define 'move' for an AI agent?

• When the agent moves from one place to another, then it is called the move of the agent
• When the agent goes from one state to another, it is known as a move

Answer: B) When the agent goes from one state to another, it is known as a move

The "move" of an agent is defined with respect to the state it changes and not with respect to its actual position.

24. "The complete set of rules for defining the valid movements of an AI agent for changing the states" What does the above definition refer to?

• Documentation for an AI agent
• Production rules for an AI agent
• Pseudo Code for an AI agent

Answer: B) Production rules for an AI agent

The production rules for an AI agent are the complete set of rules for defining the valid movements of an AI agent for changing the states.

25. State whether the following condition is true or false? "Fault tolerance of a system can be defined as the ability of a system to sustain failures and continue functioning."

The given statement is true and is the definition of Fault tolerance of the system.

26. Which of the following agents is the best in terms of AI?

• An agent which needs user inputs for solving any problem
• An agent which can solve any problem on its own without any humanintervention
• An agent which needs an exemplary similar problem defined in its knowledge base prior to the actual problem

Answer: B) An agent which can solve any problem on its own without any humanintervention

The main aim of AI is to develop a system that can solve problems on its own without any human commands or inputs. If such a system is developed, then it will be the best kind of AI system.

27. Consider the following steps:

• Gathering knowledge
• Defining problem
• Applying solution
• Forming the state space

What is the correct order for solving an AI problem?

• i. v. ii. iv. iii.
• i. ii. iii. iv. v.
• ii. i. v. iv. iii.

Answer: C) ii. i. v. iv. iii.

The correct order for solving a problem is:

• ii.Defining problem
• i.Gathering knowledge
• v.Forming the state space
• iv.Planning
• iii.Applying solution

28. Which of the mentioned options are a part of 'planning' while solving a problem by an AI agent?

• Deciding which data Structure to choose
• Forming the control strategy
• Inferring for similar problems in the knowledge base

All the mentioned options are a part of 'planning' while solving a problem by an AI agent. Planning includes tasks like deciding the data structure, Forming control strategy, and looking for solutions in the knowledge base, deciding the necessary moves to perform, etc.

29. Consider the following statement, "After all the gathering of knowledge and planning the strategies, the knowledge should be applied and the plans should be executed systematically to reach the goal state most efficiently and fruitfully." What does the above definition refer to?

• Knowledge gathering strategy
• Final step of solving the AI problem, which is applying the strategies
• State space deciding

Answer: B) Final step of solving the AI problem, which is applying the strategies

The given definition is of the final step of solving a problem in AI problem solving, which is applying the strategies.

30. Consider the following statement, "Gathering knowledge is to collect and isolate only that knowledge which is present in the Knowledge base of the agent" State whether the above condition is true or false?

Gathering knowledge means collecting knowledge both from perceiving the environment, form knowledge base and in every way possible.

31. The main Aim of the AI system is to provide a solution for real-life problems by acting and thinking humanly. Whenever an agent is confronted by a problem, what is the first step that it follows towards searching a solution to the problem?

• Searching for relevant data in the surroundings
• Searching into its own knowledge base for solutions
• Seeking for human inputs for approaching towards the solution

Answer: B) Searching into its own knowledge base for solutions

Whenever an AI-based agent is confronted by a problem, it first looks into its database for a solution or similar type of problem. Then it looks at other places like perceiving the environment, applying logic, etc.

32. Which of the following mentioned searches are heuristic searches?

• Random Search
• Depth First Search
• Breadth First Search
• Best First Search
• All i., ii., iii. and iv.
• ii. and iv.

Answer: A) Only iv.

In the best first search, which is also known as the heuristic search, the agent picks up the best node based upon the heuristic value irrespective of where the node is.

33. Which of the mentioned properties of heuristic search differentiates it from other searches?

• It provides solution in a reasonable time frame
• It provides the reasonably accurate direction to a goal
• It considers both actual costs that it took to reach the current state and approximate cost it would take to reach the goal from the current state

All the mentioned options are the properties that differentiate a heuristic search from other searches.

34. Consider the following statement: "The search first begins from the root node and the first one of the child node's sub-tree is completely traversed. That is, first all the one-sided nodes are checked, and then the other sided nodes are checked." Which search algorithm is described in the above definition?

• The Breadth First Search (BFS)
• The Depth First Search (DFS)
• The A* search

Answer: B) The Depth First Search (DFS)

In DFS, the search first begins from the root node and the first one of the child node's sub-tree is completely traversed. That is, first all the one-sided nodes are checked, and then the other sided nodes are checked.

35. Consider the following statement: "In AI search algorithms, we look for a solution which provides us the most optimized way in terms of both time and cost to reach from the current state to the Goal State." State whether the above condition is true or false?

If we want to optimize our algorithm, we must take care of both the time limit as well as the cost that occurred in our searching. The lesser they are, the more efficient our algorithm is.

36. What do the Constraints refer to in a CSP ( Constraint Satisfactory problem )?

• Restrictions
• Regulations

The constraint is the collection of all the restrictions and regulations that are imposed on the agent while solving the problem. The Agent cannot violate or avoid these restrictions while performing any action.

37. Which of the following mentioned problems are CSP (Constraint Satisfactory Problems)?

• N queens Problem
• Crypt- arithmetic problem
• Map coloring problem

Answer: B) All i., ii., iii. and iv.

All the mentioned problems are examples of CSP (Constraint Satisfactory problem) in AI.

38. Which of the mentioned properties of Constraint Satisfactory Problems dare valid?

• Constraints are a set of restrictions and regulations
• While solving a CSP, the agent cannot violate any of the rules and regulations or disobey the restrictions mentioned as the constraints
• It also focuses on reaching to the goal state

All the properties are valid as they are the properties of a CSP (Constraint Satisfactory Problem).

39. Consider the following statement: "While solving a CSP (Constraint Satisfactory Problem), the agent cannot violate any of the rules and regulations or disobey the restrictions mentioned as the constraints." Which of the following problems do not fall under the category of CSP?

• N- Queens Problem

In all the mentioned problems (or games) there are a set of constraints defined and the agent is bounded to follow the rules. Therefore, none of the mentioned problems fall apart from CSV.ache

40. Consider the following statement: "In AI, CSP are mathematical questions defined as a set of the object whose state must satisfy a number constraint or limitation." State whether the above condition is true or false?

The given definition is the actual definition of the Constraint Satisfactory Problem (CSP) and hence is true.

41. Which of the following types does the Cryptarithmetic problem belong to?

• Encryption Problem
• Constraint Satisfactory Problem
• Number problem

The Cryptarithmetic problem belongs to every type of mentioned problem: Encryption problem, Constraint satisfactory problem, as well as Number Problem.

42. Which of the following mentioned properties are valid for a Cryptarithmetic problem?

• A number 0-9 is assigned to a particular alphabet.
• Each different alphabet has a unique number.
• All the same alphabets have the same numbers.
• The numbers should satisfy all the operations that any normal number does.

All the mentioned properties are valid properties for a Cryptarithmetic problem.

43. Which of the mentioned points are not valid with respect to a Cryptarithmetic problem?

• Constraints should be taken care of while solving the problem
• The text is converted from readable format to non-readable format
• If numbers are not sufficient, we can use special symbols like $#@% to encrypt the text

Answer: C) If numbers are not sufficient, we can use special symbols like $#@% to encrypt the text

There is no rule in the Cryptarithmetic problems regarding using the special numbers for encryption. We must encrypt the text using the numbers 0-9. Hence, this encryption method is limited to small length texts only.

44. Consider the following statement: "The Cryptarithmetic problem in Artificial Intelligence is a type of encryption problem in which the written message in an alphabetical form which is easily readable and understandable is converted into a numeric form which is neither easily readable nor understandable." By reading the above statement, what are the places where this technique can be applied?

• To share passwords
• To encode number plates of vehicles
• To encode their names by students while filling the answer sheet

Answer: A) To share passwords

The Cryptarithmetic problem is an efficient way to share passwords. However, the other things mentioned do not require any encryption, so applying this technique there would be a waste of time.

45. Provide the answer for the following Cryptarithmetic problem: "SEND MORE MONEY"

• S=12; E=5; N=6; D=8; M=1; O=0; R=8; Y=2
• S=9; E=5; N=6; D=7; M=1; O=0; R=8; Y=2
• S=5; E=5; N=6; D=7; M=1; O=0; R=8; Y=2
• S=9; E=5; N=9; D=7; M=1; O=0; R=8; Y=2

Answer: B) S=9; E=5; N=6; D=7; M=1; O=0; R=8; Y=2

Cryptarithmetic problems should be solved, but here we can find the answer to this question through the options itself.

• The first option has S=12 which exceeds 0-9 range.
• The third option has the same value for S and E
• The fourth option also has the same values for S and N
• Therefore, the correct option is b.

46. Which of the following statements correctly define knowledge representation in AI?

• It is the way in which facts and information are stored in the storage system of the agent
• It is the way in which we feed the knowledge in machine understandable form
• We modify the knowledge and convert it into the format which is acceptable by the machine

All the mentioned points define the knowledge representation in AI correctly.

47. In AI systems, Knowledge can be represented in two ways. What are these two ways?

• Machine Logic
• Predicate Logic
• Propositional Logic
• Compound Logic
• i. and iii.
• ii. and iii.
• iii. and iv.

Answer: C) ii. and iii.

In an intelligent agent, the knowledge can be represented in two ways:

• Propositional logic and
• Predicate logic

48. Which of the mentioned point are not valid with respect to a Propositional Logic?

• In propositional Logic, each sentence is a declarative sentence
• In propositional logic, the sentence can have answers other than True or False
• Propositional Logic is a type of knowledge representation in AI

Answer: C) Propositional Logic is a type of knowledge representation in AI

In propositional logic, each sentence is a declarative sentence which is a sentence or proposition which is either true or false.

49. Consider the following statement: "In the propositional logic system of knowledge representation, it is assumed that the word contains object, relations, and functions. The Predicate logic is a symbolized reasoning in which we can divide the sentence into a well-defined subject and predicate." By reading the above statement, State whether it is true or false?

The given statement is false because it is not what a propositional Logic is. The given statement holds for Predicate logic.

50. What does a first order predicate logic contain?

• Predicate and a subject
• Predicate and a Preposition
• Subject and an object

Answer: A) Predicate and a subject

The Predicate logic is a symbolized reasoning in which we can divide the sentence into a well-defined subject and predicate. The subject is defined by the predicate. It should be noted that the predicate can only refer to a single subject.

51. Why do we want to implement the concept of Logic in an AI system?

• So that the agent can have decision making capability
• So that the agent can think and act humanly
• So that the agent can apply the logic for finding the solution to any particular problem

All the mentioned points are the valid reason behind- "Why we want to implement logic in an AI system?"

52. In AI systems, Logic can be represented in two types. What are these two types?

• Inductive Logic
• Common Logic
• Deductive Logic

Answer: B) i. and iii.

In artificial intelligence, we deal with two types of logics: Deductive and Inductive.

53. Which of the following statements correctly defines the deductive logic in AI?

• In deductive logic, the complete evidence is provided about the truth of the conclusion made
• A top-down approach is followed
• The agent uses specific and accurate premises that lead to a specific conclusion

All the mentioned statements correctly defined the deductive logic in AI .

54. Consider the following statement: "While taking any decision, the agent must provide specific reasons based on which the decision was taken. And this reasoning can be done by the agent only if the agent has the capability of understanding the logic." Among which of the following situations will the agent use and apply logic for solving the problem?

• To solve real life problems
• To play a game against a human in the same way as a human would do
• To understand the environment variables

In all the mentioned situations, the agent will have to apply logic to solve the problem humanly.

55. In AI, the Logic is classified into two types: deductive and inductive. Which of the following approaches is followed up by the Inductive logic?

• Top-down approach
• Bottom-up approach
• No specific approach
• According to precedence

Answer: B) Bottom-up approach

In Inductive logic, the reasoning is done through a 'bottom-up' approach. What this means is that the agent here takes specific information and them generalizes it for the sake of complete understanding.

56. How many types of quantifiers are there that are used to represent knowledge?

• User can define as many quantifiers he wants

Answer: B) 2 types

There are two types of quantifiers: Universal Quantifier and Existential Quantifier.

57. There are two types of quantifiers used to quantify the statement in the knowledge representation in AI. What are these two types of quantifiers?

• Universal Quantifiers
• Subjective Quantifiers
• Existential Quantifiers
• Selective Quantifier

There are two types of quantifiers that are used to quantify the statement in an AI system: Universal Quantifier and Existential Quantifier.

58. Which of the mentioned point correctly defines a quantifier in AI?

• Quantifiers are numbers ranging from 0-9.
• Quantifiers are the quantity defining terms which are used with the predicates.
• Quantifiers quantize the term between 0 and 1.

Answer: B) Quantifiers are the quantity defining terms which are used with the predicates.

Quantifiers are the quantity defining terms that are used with the predicates. There are two types of quantifiers that are used to quantify the statement in an AI system: Universal Quantifier and Existential Quantifier.

59. Consider the following statement: "The universal quantifier is used to define the whole subject population under the predicate." By reading the above statement, what are the phrases for which the universal quantifier can be applied?

The universal quantifier is used to define the whole subject population under the predicate. It can be used anywhere where the phrases like: 'for all', 'for each', 'for every' are used.

60. Consider the following statement: "The Existential Quantifier is used at the places where only some part of the subject's population is to be defined under the predicate." By reading the above statement, what are the phrases for which the existential quantifier can be applied?

Answer: B) For some

The Existential Quantifier is used at the places where only some part of the subject's population is to be defined under the predicate. It can be used at all the places where the following phrases are used: 'There exist', 'For some', 'For at least', etc.

61. Using how many levels can a knowledge-based agent be defined?

Answer: B) Java

The knowledge-based agent can be described using three levels. These are,

• Knowledge level
• Logical level
• Implementation level

62. There are various knowledge-based agent levels in AI. What are these levels?

• Knowledge Level
• Logical Level
• Common Sense Level
• Implementation Level
• i., ii. and iii.

Answer: B) i., ii. and iii

The knowledge-based agent can be described using three levels. These are:

63. Which of the levels in a knowledge-based agent is the most abstract level?

• Can't be determined

Answer: A) Knowledge Level

The Knowledge Level is the basic and the most abstract in a knowledge-based agent. This level describes the agent by what it knows, i.e. through its knowledge base. In this level, the information that the agent has, its goals and the utility are defined.

64. Consider the following statement: "The knowledge-based agent can be described using three levels. These are: Knowledge level, Logical level, and Implementation level" In which of these levels, is the raw and discrete information encoded into sentences?

Answer: B) Logical Level

In the logical level, the raw and discrete information which is present in the knowledge level is encoded into sentences. In simple words, the agent at this level derives the logic out of the knowledge base according to the problem.

65. Consider the following statement: "The knowledge-based agent can be described using three levels. These are: Knowledge level, Logical level, and Implementation level" Which of the following layers deals with the physical representation of the sentences?

Answer: C) Implementation Level

The Implementation level is the final layer of the knowledge-based agent. In the implementation level, the logic which the agent has derived in the logical level is brought to implementation. This layer deals with the physical representation of the sentences.

66. Which of the following is true with respect to uncertainty in AI systems?

• Uncertainty arises when we are not 100 percent confident in our decisions
• Whenever uncertainty arises, there is needs to be an estimation taken fo getting to any conclusion
• The AI agent should take certain decisions even in the situations of uncertainty

All the mentioned points are true and valid with respect to uncertainty in AI systems. Also, each point is very important while dealing with uncertainty in AI agents.

67. Which of the following mentioned statements are uncertain?

• The number occurred on rolling a die.
• What will the temperature tomorrow?
• What card will be get on picking a card from a fair deck of 52 cards?
• What output will we get on tossing a coin?

We cannot be 100% sure about the output we get on tossing a die, coin or picking a card, or the upcoming day's temperature as it depends on various factors which are almost impossible to monitor accurately. Hence we have uncertainty there.

68. Which of the mentioned points are valid reasons for uncertainty in the nature?

• Partially observable environment
• Dynamic nature of the environment
• Inaccessible area in the environment

All the mentioned reasons are valid as they are responsible for causing uncertainty in the environment.

69. Consider the following statement: "When we talk about perceiving information from the environment, then the main problem that arises is that there is always some uncertainty is our observations. This is because the world is an enormous entity and the surroundings that we take under study are not always well defined. So, there is needs to be an estimation taken for getting to any conclusion." Which among the following takes the best decisions in a situation with uncertainty?

• AI based agents

Answer: B) Humans

Human being face this uncertainty daily that too many times. But still, they manage to make successful decisions. This is because humans have strong estimating and decision making power and their brains function in such a way that every time such a situation arises, the alternative with the maximum positive output is chosen.

70. State whether the following condition is true or false? "The cases of uncertainty mostly happen in those cases where the conditions are neither completely true nor completely false."

Uncertainty arises when we are not 100 percent sure about the outcome of the decisions. This mostly happens in those cases where the conditions are neither completely true nor completely false.

71. Which of the following correctly defines the use of probabilistic reasoning in AI systems?

• In situations of uncertainty, probabilistic theory can help us give an estimate of how much an event is likely to occur or happen.
• It helps to find the probability whether the agent should do the task or not.
• It does not help at all.
• None of the above.

Answer: A) In situations of uncertainty, probabilistic theory can help us give an estimate of how much an event is likely to occur or happen.

The only option (A) is the valid reason which correctly defines the use of probabilistic reasoning in AI systems.

72. On which of the mentioned points does the Probabilistic Reasoning depend?

• Observations

All the mentioned reasons are valid as the Probabilistic reasoning depends upon all of them.

73. The results that we get after we apply probabilistic reasoning to a problem are,

• 100% accurate
• Estimated values
• Wrong values

Answer: B) Estimated values

Probabilistic theory helps us to derive an estimate about how much an event is likely to occur or happen.

74. State whether the following condition is true or false? "The sum of all these probabilities for an experiment is always 1 because all these events/alternatives can happen only within this experiment."

It is the basic and most important law of probability that the sum of probabilities for an experiment is always 1.

75. Which of the following points are valid with respect to conditional probability?

• Conditional Probability gives 100% accurate results.
• Conditional Probability can be applied to a single event.
• Conditional Probability has no effect or relevance or independent events.

Answer: C) Conditional Probability has no effect or relevance or independent events.

Independent events are those events that neither cause any effect nor are affected by the occurrence of some other event. Hence, the Conditional Probability has no effect or relevance on the independent events.

76. Among which of the following mentioned statements will the conditional probability be applied?

• The number occurred on rolling a die one time.
• What card will get on picking a card from a fair deck of 52 cards?
• What output will we get on tossing a coin once?

Answer: D) Only ii.

The upcoming day's temperature as it depends on various factors such as wind speed, current temperature, and humidity level, etc. So, the future weather conditions are dependent upon these factors and thus it is a dependent event and hence conditional property can be applied to it.

77. On which of the mentioned points does the Conditional Probability reasonable to apply?

• Dependent Events
• Independent Events
• Neither a. nor b.
• Both a. and b.

Answer: A) Dependent Events

78. The results that we get after we apply conditional probability to a problem are,

Like all other probabilistic theory methods, conditional probability also helps us to derive an estimate about how much an event is likely to occur or happen.

79. State whether the following condition is true or false? "The independent events are affected by the happening of some other events which may occur simultaneously or have occurred before it."

The given statement is false because dependent events are affected by the happening of some other events which may occur simultaneously or have occurred before it, not the independent events.

80. Bayesian Theorem was named after its inventor. Who invented the Bayesian theorem?

• Reverend Thomas Bayes
• Stuart Bayes Hamilton
• Bayes Canney

Answer: A) Reverend Thomas Bayes

Bayes' theorem was given by Reverend Thomas Bayes and thus named after him.

81. Among which of the following mentioned statements can the Bayesian probability be applied?

• In the cases, where we have one event
• In the cases, where we have two events
• In the cases, where we have three events
• In the cases, where we have more than three events

Bayes' Theorem is applicable only in those experiments where we have only two events. It does not apply to the cases where the number of events is more than two.

82. On which of the mentioned points is the Bayesian theorem reasonable to apply?

The Bayesian theorem is used to find the conditional probability. As the Conditional Probability has no effect or relevance on the independent events and is relevant only for dependent events, the Bayesian theorem also applies for the same.

83. The results that we get after we apply Bayesian Theorem to a problem are,

Like all other probabilistic theory methods like conditional probability, the Bayesian theorem also helps us to derive an estimate about how much an event is likely to occur or happen.

84. State whether the following condition is true or false? "In Bayesian theorem, it is important to find the probability of both the events occurring simultaneously."

It should be noted that in the Bayesian equation, we need not find the probability of both the events occurring simultaneously, i.e. P(A^B). We can calculate the same using the below Bayesian equation,

85. Which of the following statements correctly define the concept of Inference in AI?

• When we conclude the facts and figures to reach a particular decision, that is called inference
• All of the above.

Answer: B) When we conclude the facts and figures to reach a particular decision, that is called inference

Inference means finding a conclusion based on facts, information, and evidences. In simple words, when we conclude the facts and figures to reach a particular decision, that is called inference.

86. Which of the following are valid inference rules that are used in Inference?

• Subtraction
• Multiplication

Answer: A) Only i.

The addition is a valid inference rule. Apart from these, there are further many inference rules such as simplification, modus ponens, modus tollens, etc.

87. Which of the mentioned rules are valid Inference rules?

• Modus Ponens
• Backward Chaining

All the mentioned rules are valid inference rules. Apart from these, there are further many inference rules such as simplification, Addition, forward chaining, modus tollens, etc.

88. Consider the following statement: "In the reasoning by resolution, we are given the goal condition and available facts and statements. Using these facts and statements, we have to decide whether the goal condition is true or not." By reading the above statement, state whether it is true or false?

The above statement is True.

89. Which of the following are a deductive type of Inference rule?

• Forward Chaining
• Both A. and B.

Answer: C) Both A. and B.

Both forward chaining and backward chaining are types of deductive inference rules.

90. Which of the following statements define the certainty factor accurately?

• The certainty factor is same as the probability of any event
• The Certainty Factor (CF) is a numeric value that tells us about how likely an event or a statement is supposed to be true
• The Certainty Factor (CF) is a numeric value that tells us about how certain we are about performing a particular task

Answer: B) The Certainty Factor (CF) is a numeric value that tells us about how likely an event or a statement is supposed to be true

The statement (B) define the certainty factor accurately.

91. "The Certainty Factor (CF) is a numeric value which tells us about how likely an event or a statement is supposed to be true." What is the range of this numeric value, i.e. Certainty Factor?

• Between 0 to 1 (Both inclusive)
• Between 0 to 1 (Both exclusive)
• Between -1 to +1

Answer: C) Between -1 to +1

The value of the Certainty factor lies between -1.0 to +1.0, where the negative 1.0 value suggests that the statement can never be true in any situation, and the positive 1.0 value defines that the statement can never be false.

92. Consider the following statement: "The value of the Certainty factor lies between -1.0 to +1.0, where the negative 1.0 value suggests that the statement can never be true in any situation, and the positive 1.0 value defines that the statement can never be false." What does the value 0 denote for CF?

• Half true Half False
• Somewhat true but not entirely false
• Agent has no information about the event

Answer: C) Agent has no information about the event

The value 0 suggests that the agent has no information about the event or the situation.

93. What is the minimum Certainty factor which decided whether the value is true or false?

• Is decided in prior to every problem

Answer: D) Is decided in prior to every problem

A minimum Certainty factor is decided for every case through which the agent decides whether the statement is true or false.

94. State whether the following condition is true or false: "The value of the Certainty factor lies between -1.0 to +1.0, where the negative 1.0 value suggests that the statement can never be true in any situation, and the positive 1.0 value defines that the statement can never be false."

The given condition is True.

95. Which of the mentioned statements are true with respect to Fuzzy logic in AI?

• Fuzzy Logic (FL) is a method by which any expert system or any agent based on Artificial Intelligence performs reasoning under uncertain conditions.
• In this method, the reasoning is done in almost the same way as it is done in humans.
• In this method, all the possibilities between 0 and 1 are drawn.

All the mentioned statements are true and valid with respect to Fuzzy Logic in AI systems.

96. Which of the following hold in the Fuzzy Logic System? Choose from the following options?

• Well defined inference making model
• Complex Decision making can be easily performed
• It is easily understandable
• All i, ii. and iii.

Answer: D) All i, ii. and iii.

All the mentioned points are valid and hold true for a fuzzy Logic AI system.

97. Which of the following are a part of Fuzzy Logic System? Choose from the following options?

• Knowledge base
• Fuzzification Module
• Inference Engine
• Defuzzification Module
• All i, ii. and iii. and iv.

Answer: D) All i, ii. and iii. and iv.

If we take a look at the architecture of the Fuzzy Logic system, then we find that it is composed of following four major parts:

• Knowledge Base

98. Consider the following statement: "Through the Fuzzy Logic, The Agent can handle situations like incomplete data, imprecise knowledge, etc." By reading the above statement, State whether it is true or false?

In the Fuzzy Set Theory, the inference-making process and other concluding methods are well defined using algorithms that the agent or any computer system can easily understand. Thus the Agent in this method can handle situations like incomplete data, imprecise knowledge, etc.

99. Fuzzy Logic is a convenient way of representing which of the following situations?

• Partially True and Partially False
• Completely True and Completely False

Answer: A) Partially True and Partially False

Fuzzy Logic is an effective and convenient way for representing the situation where the results are partially true or partially false instead of being completely true or completely false.

100. Which of the following mentioned statements are the valid reasons for implementing the learning part in the systems in AI?

• To implement humanly behavior.
• To deal with unknown environment.
• To improve the reasoning capability of the agent.

All the mentioned statements are true and valid reasons for implementing the learning part in the AI systems.

101. Which of the following streams of AI deal with the learning part of the machine (AI systems)? Choose from the following options?

• Cloud computing
• Humanly Learning

The stream of AI that deals with the learning part of the machine (AI systems) is Machine Learning.

102. How many types of learning are there for an AI system?

Answer: B) 3 types

The Learning process in the AI agent is broadly classified into three types. They are:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning (or semi supervised learning)

103. Consider the following statement: "Semi-supervised learning is a type of learning which falls between supervised and unsupervised learning." By reading the above statement, Choose from the following?

• Yes, the given statement is true
• No, the given statement is false as there is nothing such as semi-supervised learning

Answer: A) Yes, the given statement is true

The given statement: "Semi-supervised learning is a type of learning which falls between supervised and unsupervised learning" is true.

104. How does the agent learn through its surroundings?

• By perceiving the environment through sensors
• By human's sensory organs

Answer: A) By perceiving the environment through sensors

The agent implements the learning part from its surroundings through its sensors, like the camera, audio input devices, temperature sensors etc.

105. What is the major problem for AI systems while solving the real world problems?

• Uncertainty in Environment
• Poor battery life of the system
• Improper training time

Answer: A) Uncertainty in Environment

Uncertainty in environment is the biggest problem for AI systems while solving the real-world problems.

106. Which of the following are valid methods used for decision making by an AI agent in situations of uncertainty? Choose from the following options?

• Probabilistic theory
• Fuzzy logic
• Truth Maintenance

The following three are among the basic and widely used methods to perform decision making while handling the uncertainty:

• Probabilistic Theory
• Fuzzy Logic

107. Which among the following does fuzzy logic refer to the most?

• Probability
• Heuristic Search

Answer: A) Probability

Fuzzy Logic is also somewhat similar to Probability theory. In fuzzy logic also, all the possible values between the range 0 to 1 are considered and the system produces a real number lying between this range which determines the possibility of the event.

108. Consider the following statement: "In most of the cases, the conditions appear to be partially true. So, their representation was not possible in the Boolean knowledge representation. Hence other methods to deal with this were invented so that uncertainty situations can be dealt with" By reading the above statement, Choose from the following?

The given statement is True.

109. The Maintenance system in the Truth maintenance method stores what?

• Only valid data
• Only invalid data
• Both valid and invalid data

Answer: A) Only valid data

Maintenance System keeps a record of the data which is valid till now. The data which becomes invalid is either moved to trash or is stored in some other file.

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problem solving by searching

34 questions

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No student devices needed.   Know more

1. What is the main task of a problem-solving agent?

a) Solve the given problem and reach to goal

b) To find out which sequence of action will get it to the goal state

c) All of the mentioned

d) None of the mentioned

2. What is state space?

a) The whole problem

b) Your Definition to a problem

c) Problem you design

d) Representing your problem with variable and parameter

3. The problem-solving agent with several immediate options of unknown value can decide what to do by just examining different possible sequences of actions that lead to states of known value, and then choosing the best sequence. This process of looking for such a sequence is called Search.

4. A search algorithm takes _________ as an input and returns ________ as an output.

a) Input, output

b) Problem, solution

c) Solution, problem

 d) Parameters, sequence of actions

5. A problem in a search space is defined by one of these state.

a) Initial state

b) Last state

c) Intermediate state

d) All of the mentioned

6. The Set of actions for a problem in a state space is formulated by a ___________

a) Intermediate states

b) Initial state

c) Successor function, which takes current action and returns next immediate state

7. A solution to a problem is a path from the initial state to a goal state. Solution quality is measured by the path cost function, and an optimal solution has the highest path cost among all solutions.

8. What is the major component/components for measuring the performance of problem solving?

a) Completeness

b) Optimality

c) Time and Space complexity

9.Which is the best way to go for Game playing problem?

a) Linear approach

b) Heuristic approach (Some knowledge is stored)

c) Random approach

d) An Optimal approach

10.Which search strategy is also called as blind search?

a) Uninformed search

b) Informed search

c) Simple reflex search

How many types are available in uninformed search method?

Which search is implemented with an empty first-in-first-out queue?

a) Depth-first search

b) Breadth-first search

c) Bidirectional search

When is breadth-first search is optimal?

a) When there is less number of nodes

b) When all step costs are equal

c) When all step costs are unequal

What is the space complexity of Depth-first search?

How many parts does a problem consists of?

Which search algorithm imposes a fixed depth limit on nodes?

a) Depth-limited search

b) Depth-first search

c) Iterative deepening search

d) Bidirectional search

Which search implements stack operation for searching the states?

c) Breadth-first search

What is the other name of informed search strategy?

a) Simple search

b) Heuristic search

c) Online search

How many types of informed search method are in artificial intelligence?

Which search uses the problem specific knowledge beyond the definition of the problem?

a) Informed search

d) Uninformed search

Which function will select the lowest expansion node at first for evaluation?

a) Greedy best-first search

b) Best-first search

c) Depth-first search

What is the heuristic function of greedy best-first search?

a) f(n) != h(n

b) f(n) < h(n)

c) f(n) = h(n)

d) f(n) > h(n)

Which search is complete and optimal when h(n) is consistent?

a) Best-first search

c) Both Best-first & Depth-first search

d) A* search

Which is used to improve the performance of heuristic search?

a) Quality of nodes

b) Quality of heuristic function

c) Simple form of nodes

Which search method will expand the node that is closest to the goal?

b) Greedy best-first search

c) A* search

A heuristic is a way of trying ___________

a) To discover something or an idea embedded in a program

b) To search and measure how far a node in a search tree seems to be from a goal

c) To compare two nodes in a search tree to see if one is better than another

A* algorithm is based on ___________

a) Breadth-First-Search

b) Depth-First –Search

c) Best-First-Search

d) Hill climbing

A* is optimal if h(n) is an admissible heuristic-that is, provided that h(n) never underestimates the cost to reach the goal.

What is the evaluation function in A* approach?

a) Heuristic function

b) Path cost from start node to current node

c) Path cost from start node to current node + Heuristic cost

d) Average of Path cost from start node to current node and Heuristic cost

What is the space complexity of Greedy search?

Heuristic function h(n) is ________

a) Lowest path cost

b) Cheapest path from root to goal node

c) Estimated cost of cheapest path from root to goal node

d) Average path cost

The name “best-first search” is a venerable but inaccurate one. After all, if we could really expand the best node first, it would not be a search at all; it would be a straight march to the goal. All we can do is choose the node that appears to be best according to the evaluation function.

Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion.

a) Evaluation function returning lowest evaluation

b) Evaluation function returning highest evaluation

c) Evaluation function returning lowest & highest evaluation

d) None of them is applicable

Best-First search can be implemented using the following data structure.

c) Priority Queue

d) Circular Queue

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[MCQ,s] Artificial Intelligence & Soft Computing

Introduction to artificial intelligence(ai) and soft computing, problem solving, knowledge, reasoning and planning, fuzzy logic, artificial neural network, expert system, artificial intelligence & soft computing.

1. What is the main task of a problem-solving agent? A. Solve the given problem and reach to goal B. To find out which sequence of action will get it to the goal state C. Both A and B D. None of the Above Ans : C Explanation: The problem-solving agents are one of the goal-based agents

2. What is Initial state + Goal state in Search Terminology? A. Problem Space B. Problem Instance C. Problem Space Graph D. Admissibility Ans : B Explanation: Problem Instance : It is Initial state + Goal state.

3. What is Time Complexity of Breadth First search algorithm? A. b B. b^d C. b^2 D. b^b Ans : B Explanation: Time Complexity of Breadth First search algorithm is b^d.

4. Depth-First Search is implemented in recursion with _______ data structure. A. LIFO B. LILO C. FIFO D. FILO Ans : A Explanation: Depth-First Search implemented in recursion with LIFO stack data structure.

5. How many types are available in uninformed search method? A. 2 B. 3 C. 4 D. 5 Ans : D Explanation: The five types of uninformed search method are Breadth-first, Uniform-cost, Depth-first, Depth-limited and Bidirectional search.

6. Which data structure conveniently used to implement BFS? A. Stacks B. Queues C. Priority Queues D. None of the Above Ans : B Explanation: Queue is the most convenient data structure, but memory used to store nodes can be reduced by using circular queues.

7. How many types of informed search method are in artificial intelligence? A. 2 B. 3 C. 4 D. 5 Ans : C Explanation: The four types of informed search method are best-first search, Greedy best-first search, A* search and memory bounded heuristic search.

8. Greedy search strategy chooses the node for expansion in ___________ A. Shallowest B. Deepest C. The one closest to the goal node D. Minimum heuristic cost Ans : C Explanation: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It depends upon the application on which the algorithm is applied.

9. What is disadvantage of Greedy Best First Search? A. This algorithm is neither complete, nor optimal. B. It can get stuck in loops. It is not optimal. C. There can be multiple long paths with the cost ≤ C* D. may not terminate and go on infinitely on one path Ans : B Explanation: The disadvantage of Greedy Best First Search is that it can get stuck in loops. It is not optimal.

10. Searching using query on Internet is, use of ___________ type of agent. A. Offline agent B. Online Agent C. Goal Based D. Both B and C Ans : D Explanation: Refer to the definitions of both the type of agent.

11. An AI system is composed of? A. agent B. environment C. Both A and B D. None of the Above Ans : C Explanation: An AI system is composed of an agent and its environment.

12. Which instruments are used for perceiving and acting upon the environment? A. Sensors and Actuators B. Sensors C. Perceiver D. Perceiver and Sensor Ans : A Explanation: An agent is anything that can be viewed as perceiving and acting upon the environment through the sensors and actuators.

13. Which of the following is not a type of agents in artificial intelligence? A. Model based B. Utility based C. Simple reflex D. target based Ans : D Explanation: The four types of agents are Simple reflex, Model based, Goal based and Utility based agents.

14. Which is used to improve the agents performance? A. Perceiving B. Observing C. Learning D. Sequence Ans : C Explanation: An agent can improve its performance by storing its previous actions.

15. Rationality of an agent does not depends on? A. performance measures B. Percept Sequence C. reaction D. actions Ans : C Explanation: Rationality of an agent does not depends on reaction

16. Agent’s structure can be viewed as ? A. Architecture B. Agent Program C. Architecture + Agent Program D. None of the Above Ans : C Explanation: Agent’s structure can be viewed as – Agent = Architecture + Agent Program

17. What is the action of task environment in artificial intelligence? A. Problem B. Solution C. Agent D. Observation Ans : A Explanation: Task environments will pose a problem and rational agent will find the solution for the posed problem.

18. What kind of environment is crossword puzzle? A. Dynamic B. Static C. Semi Dynamic D. Continuous Ans : B Explanation: As the problem in crossword puzzle are posed at beginning itself, So it is static.

19. What could possibly be the environment of a Satellite Image Analysis System? A. Computers in space and earth B. Image categorization techniques C. Statistical data on image pixel intensity value and histograms D. All of the above Ans : D Explanation: An environment is something which agent stays in.

20. Which kind of agent architecture should an agent an use? A. Relaxed B. Relational C. Both A and B D. None of the AboveAns : C Explanation: Because an agent may experience any kind of situation, So that an agent should use all kinds of architecture.

21. Which depends on the percepts and actions available to the agent? a) Agent b) Sensor c) Design problem d) None of the mentioned Answer: c Explanation: The design problem depends on the percepts and actions available to the agent, the goals that the agent’s behavior should satisfy.

22. Which were built in such a way that humans had to supply the inputs and interpret the outputs? a) Agents b) AI system c) Sensor d) Actuators Answer: b Explanation: AI systems were built in such a way that humans had to supply the inputs and interpret the outputs.

23. Which technology uses miniaturized accelerometers and gyroscopes? a) Sensors b) Actuators c) MEMS d) None of the mentioned Answer: c Explanation: Micro ElectroMechanical System uses miniaturized accelerometers and gyroscopes and is used to produce actuators.

24. What is used for tracking uncertain events? a) Filtering algorithm b) Sensors c) Actuators d) None of the mentioned Answer: a Explanation: Filtering algorithm is used for tracking uncertain events because in this the real perception is involved.

25. What is not represented by using propositional logic? a) Objects b) Relations c) Both Objects & Relations d) None of the mentioned Answer: c Explanation: Objects and relations are not represented by using propositional logic explicitly.

26. Which functions are used as preferences over state history? a) Award b) Reward c) Explicit d) Implicit Answer: b Explanation: Reward functions may be that preferences over states are really compared from preferences over state histories.

27. Which kind of agent architecture should an agent an use? a) Relaxed b) Logic c) Relational d) All of the mentioned Answer: d Explanation: Because an agent may experience any kind of situation, So that an agent should use all kinds of architecture.

28. Specify the agent architecture name that is used to capture all kinds of actions. a) Complex b) Relational c) Hybrid d) None of the mentioned Answer: c Explanation: A complete agent must be able to do anything by using hybrid architecture.

29. Which agent enables the deliberation about the computational entities and actions? a) Hybrid b) Reflective c) Relational d) None of the mentioned Answer: b Explanation: Because it enables the agent to capture within itself.

30. What can operate over the joint state space? a) Decision-making algorithm b) Learning algorithm c) Complex algorithm d) Both Decision-making & Learning algorithm

31. What is the action of task environment in artificial intelligence? a) Problem b) Solution c) Agent d) Observation Answer: a Explanation: Task environments will pose a problem and rational agent will find the solution for the posed problem.

32. What is the expansion if PEAS in task environment? a) Peer, Environment, Actuators, Sense b) Perceiving, Environment, Actuators, Sensors c) Performance, Environment, Actuators, Sensors d) None of the mentioned Answer: c Explanation: Task environment will contain PEAS which is used to perform the action independently.

33. What kind of observing environments are present in artificial intelligence? a) Partial b) Fully c) Learning d) Both Partial & Fully Answer: d Explanation: Partial and fully observable environments are present in artificial intelligence.

34. What kind of environment is strategic in artificial intelligence? a) Deterministic b) Rational c) Partial d) Stochastic Answer: a Explanation: If the environment is deterministic except for the action of other agent is called deterministic.

35. What kind of environment is crossword puzzle? a) Static b) Dynamic c) Semi Dynamic d) None of the mentioned Answer: a Explanation: As the problem in crossword puzzle are posed at beginning itself, So it is static.

36. What kind of behavior does the stochastic environment posses? a) Local b) Deterministic c) Rational d) Primary Answer: a Explanation: Stochastic behavior are rational because it avoids the pitfall of predictability.

37. Which is used to select the particular environment to run the agent? a) Environment creator b) Environment Generator c) Both Environment creator & Generator d) None of the mentioned Answer: b Explanation: None.

38. Which environment is called as semi dynamic? a) Environment does not change with the passage of time b) Agent performance changes c) Environment will be changed d) Environment does not change with the passage of time, but Agent performance changes Answer: d Explanation: If the environment does not change with the passage of time, but the agent performance changes by time.

39. Where does the performance measure is included? a) Rational agent b) Task environment c) Actuators d) Sensor Answer: b Explanation: In PEAS, Where P stands for performance measure which is always included in task environment.

40. Which is used to provide the feedback to the learning element? a) Critic b) Actuators c) Sensor d) None of the mentioned Answer: a Explanation: The learning element gets the feedback from the critic which is presented in the environment on how the agent is doing.

41: Who initiated the idea of Soft Computing A.Charles Darwin B.Lofti A Zadeh C.Rechenberg D.Mc_Culloch Ans:B. Lofti A Zadeh

42.Fuzzy Computing A.mimics human behaviour B.doesnt deal with 2 valued logic C.deals with information which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic D.All of the above Ans :D. All of the above

43:Neural Computing A.mimics human brain B.information processing paradigm C.Both (a) and (b) D.None of the above Ans : C. Both (a) and (b)

44:Genetic Algorithm are a part of A.Evolutionary Computing B.inspired by Darwin’s theory about evolution – “survival of the fittest” C.are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics D.All of the above Ans : D. All of the above

45.What are the 2 types of learning A.Improvised and unimprovised B.supervised and unsupervised C.Layered and unlayered D.None of the above Ans: B. supervised and unsupervised

46:Supervised Learning is A.learning with the help of examples B.learning without teacher C.learning with the help of teacher D.learning with computers as supervisor Ans:C. learning with the help of teacher

47.Unsupervised learning is A.learning without computers B.problem based learning C.learning from environment D.learning from teachers Ans: C. learning from environment

48:Conventional Artificial Intelligence is different from soft computing in the sense A.Conventional Artificial Intelligence deal with prdicate logic where as soft computing deal with fuzzy logic B.Conventional Artificial Intelligence methods are limited by symbols where as soft computing is based on empirical data C.Both (a) and (b) D.None of the above Ans:C. Both (a) and (b)

1. Which search strategy is also called as blind search? a) Uninformed search b) Informed search c) Simple reflex search d) All of the mentioned Answer: a Explanation: In blind search, We can search the states without having any additional information. So uninformed search method is blind search.

2. How many types are available in uninformed search method? a) 3 b) 4 c) 5 d) 6 Answer: c Explanation: The five types of uninformed search method are Breadth-first, Uniform-cost, Depth-first, Depth-limited and Bidirectional search.

3. Which search is implemented with an empty first-in-first-out queue? a) Depth-first search b) Breadth-first search c) Bidirectional search d) None of the mentioned Answer: b Explanation: Because of FIFO queue, it will assure that the nodes that are visited first will be expanded first.

4. When is breadth-first search is optimal? a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal d) None of the mentioned Answer: b Explanation: Because it always expands the shallowest unexpanded node.

5. How many successors are generated in backtracking search? a) 1 b) 2 c) 3 d) 4 Answer: a Explanation: Each partially expanded node remembers which successor to generate next because of these conditions, it uses less memory.

6. What is the space complexity of Depth-first search? a) O(b) b) O(bl) c) O(m) d) O(bm) Answer: d Explanation: O(bm) is the space complexity where b is the branching factor and m is the maximum depth of the search tree.

7. How many parts does a problem consists of? a) 1 b) 2 c) 3 d) 4 Answer: d Explanation: The four parts of the problem are initial state, set of actions, goal test and path cost.

8. Which algorithm is used to solve any kind of problem? a) Breadth-first algorithm b) Tree algorithm c) Bidirectional search algorithm d) None of the mentioned Answer: b Explanation: Tree algorithm is used because specific variants of the algorithm embed different strategies.

9. Which search algorithm imposes a fixed depth limit on nodes? a) Depth-limited search b) Depth-first search c) Iterative deepening search d) Bidirectional search Answer: a Explanation: None.

10. Which search implements stack operation for searching the states? a) Depth-limited search b) Depth-first search c) Breadth-first search d) None of the mentioned Answer: b

11. What is the other name of informed search strategy? a) Simple search b) Heuristic search c) Online search d) None of the mentioned Answer: b Explanation: A key point of informed search strategy is heuristic function, So it is called as heuristic function.

12. How many types of informed search method are in artificial intelligence? a) 1 b) 2 c) 3 d) 4 Answer: d Explanation: The four types of informed search method are best-first search, Greedy best-first search, A* search and memory bounded heuristic search.

13. Which search uses the problem specific knowledge beyond the definition of the problem? a) Informed search b) Depth-first search c) Breadth-first search d) Uninformed search Answer: a Explanation: Informed search can solve the problem beyond the function definition, So does it can find the solution more efficiently.

14. Which function will select the lowest expansion node at first for evaluation? a) Greedy best-first search b) Best-first search c) Depth-first search d) None of the mentioned Answer: b Explanation: The lowest expansion node is selected because the evaluation measures distance to the goal.

15. What is the heuristic function of greedy best-first search? a) f(n) != h(n) b) f(n) < h(n) c) f(n) = h(n) d) f(n) > h(n) Answer: c Explanation: None.

16. Which search uses only the linear space for searching? a) Best-first search b) Recursive best-first search c) Depth-first search d) None of the mentioned Answer: b Explanation: Recursive best-first search will mimic the operation of standard best-first search, but using only the linear space.

17. Which method is used to search better by learning? a) Best-first search b) Depth-first search c) Metalevel state space d) None of the mentioned Answer: c Explanation: This search strategy will help to problem solving efficiency by using learning.

18. Which search is complete and optimal when h(n) is consistent? a) Best-first search b) Depth-first search c) Both Best-first & Depth-first search d) A* search Answer: d Explanation: None.

19. Which is used to improve the performance of heuristic search? a) Quality of nodes b) Quality of heuristic function c) Simple form of nodes d) None of the mentioned Answer: b Explanation: Good heuristic can be constructed by relaxing the problem, So the performance of heuristic search can be improved.

20. Which search method will expand the node that is closest to the goal? a) Best-first search b) Greedy best-first search c) A* search d) None of the mentioned Answer: b Explanation: Because of using greedy best-first search, It will quickly lead to the solution of the problem.

21. In many problems the path to goal is irrelevant, this class of problems can be solved using ____________ a) Informed Search Techniques b) Uninformed Search Techniques c) Local Search Techniques d) Informed & Uninformed Search Techniques Answer: c Explanation: If the path to the goal does not matter, we might consider a different class of algorithms, ones that do not worry about paths at all. Local search algorithms operate using a single current state (rather than multiple paths) and generally move only to neighbors of that state.

22. Though local search algorithms are not systematic, key advantages would include __________ a) Less memory b) More time c) Finds a solution in large infinite space d) Less memory & Finds a solution in large infinite space Answer: d Explanation: Two advantages: (1) they use very little memory-usually a constant amount; and (2) they can often find reasonable solutions in large or infinite (continuous) state spaces for which systematic algorithms are unsuitable.

23. A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. a) True b) False Answer: a Explanation: An algorithm is complete if it finds a solution if exists and optimal if finds optimal goal (minimum or maximum).

24. _______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill. a) Up-Hill Search b) Hill-Climbing c) Hill algorithm d) Reverse-Down-Hill search Answer: b Explanation: Refer the definition of Hill-Climbing approach.

25. When will Hill-Climbing algorithm terminate? a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) All of the mentioned Answer: c Explanation: When no neighbor is having higher value, algorithm terminates fetching local min/max.

26. What are the main cons of hill-climbing search? a) Terminates at local optimum & Does not find optimum solution b) Terminates at global optimum & Does not find optimum solution c) Does not find optimum solution & Fail to find a solution d) Fail to find a solution View Answer Answer: a Explanation: Algorithm terminates at local optimum values, hence fails to find optimum solution.

27. Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False Answer: a Explanation: Refer to the definition of variants of hill-climbing search.

28. Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next. a) Needy local search b) Heuristic local search c) Greedy local search d) Optimal local search Answer: c Explanation: None.

29. Hill-Climbing approach stuck for which of the following reasons? a) Local maxima b) Ridges c) Plateaux d) All of the mentioned Answer: d Explanation: Local maxima: a local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum. Ridges: Ridges result in a sequence of local maxima that is very difficult for greedy algorithms to navigate. Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat. Python Programming for Complete Beginners Start your Programming Journey with Python Programming which is Easy to Learn and Highly in Demand Click Here! 30. ___________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search Answer: b Explanation: Refer to the definition of Local Beam Search algorithm.

31. A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state. a) True b) False Answer: a Explanation: Stochastic beam search, analogous to stochastic hill climbing, helps to alleviate this problem. Instead of choosing the best k from the pool of candidate successors, stochastic beam search chooses k successors at random, with the probability of choosing a given successor being an increasing function of its value.

32. What are the two main features of Genetic Algorithm? a) Fitness function & Crossover techniques b) Crossover techniques & Random mutation c) Individuals among the population & Random mutation d) Random mutation & Fitness function Answer: a Explanation: Fitness function helps choosing individuals from the population and Crossover techniques defines the offspring generated.

1. There exist only two types of quantifiers, Universal Quantification and Existential Quantification. a) True b) False Answer: a Explanation: None.

2. Translate the following statement into FOL. “For every a, if a is a philosopher, then a is a scholar” a) ∀ a philosopher(a) scholar(a) b) ∃ a philosopher(a) scholar(a) c) All of the mentioned d) None of the mentioned Answer: a Explanation: None.

3. A _________ is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula. a) Deductive Systems b) Inductive Systems c) Reasoning with Knowledge Based Systems d) Search Based Systems Answer: a Explanation: Refer the definition of Deductive based systems.

4. The statement comprising the limitations of FOL is/are ____________ a) Expressiveness b) Formalizing Natural Languages c) Many-sorted Logic d) All of the mentioned Answer: d Explanation: The Löwenheim–Skolem theorem shows that if a first-order theory has any infinite model, then it has infinite models of every cardinality. In particular, no first-order theory with an infinite model can be categorical. Thus there is no first-order theory whose only model has the set of natural numbers as its domain, or whose only model has the set of real numbers as its domain. Many extensions of first-order logic, including infinitely logics and higher-order logics, are more expressive in the sense that they do permit categorical axiomatizations of the natural numbers or real numbers. This expressiveness comes at a meta-logical cost, however: by Lindström’s theorem, the compactness theorem and the downward Löwenheim–Skolem theorem cannot hold in any logic stronger than first-order. Formalizing Natural Languages : First-order logic is able to formalize many simple quantifier constructions in natural language, such as “every person who lives in Perth lives in Australia”. But there are many more complicated features of natural language that cannot be expressed in (single-sorted) first-order logic. Many-sorted Logic: Ordinary first-order interpretations have a single domain of discourse over which all quantifiers range. Many-sorted first-order logic allows variables to have different sorts, which have different domains.

5. A common convention is: • is evaluated first • and are evaluated next • Quantifiers are evaluated next • is evaluated last. a) True b) False Answer: a Explanation: None.

6. A Term is either an individual constant (a 0-ary function), or a variable, or an n-ary function applied to n terms: F(t1 t2 ..tn). a) True b) False Answer: a Explanation: Definition of term in FOL.

7. First Order Logic is also known as ___________ a) First Order Predicate Calculus b) Quantification Theory c) Lower Order Calculus d) All of the mentioned Answer: d Explanation: None.

8. The adjective “first-order” distinguishes first-order logic from ___________ in which there are predicates having predicates or functions as arguments, or in which one or both of predicate quantifiers or function quantifiers are permitted. a) Representational Verification b) Representational Adequacy c) Higher Order Logic d) Inferential Efficiency Answer: c Explanation: None.

9. Which is created by using single propositional symbol? a) Complex sentences b) Atomic sentences c) Composition sentences d) None of the mentioned Answer: b Explanation: Atomic sentences are indivisible syntactic elements consisting of single propositional symbol.

10. Which is used to construct the complex sentences? a) Symbols b) Connectives c) Logical connectives d) All of the mentioned Answer: c Explanation: None.

11. How many proposition symbols are there in artificial intelligence? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: The two proposition symbols are true and false.

12. How many logical connectives are there in artificial intelligence? a) 2 b) 3 c) 4 d) 5 Answer: d Explanation: The five logical symbols are negation, conjunction, disjunction, implication and biconditional.

13. Which is used to compute the truth of any sentence? a) Semantics of propositional logic b) Alpha-beta pruning c) First-order logic d) Both Semantics of propositional logic & Alpha-beta pruning Answer: a Explanation: Because the meaning of the sentences is really needed to compute the truth.

14. Which are needed to compute the logical inference algorithm? a) Logical equivalence b) Validity c) Satisfiability d) All of the mentioned Answer: d Explanation: Logical inference algorithm can be solved be using logical equivalence, Validity and satisfiability.

15. From which rule does the modus ponens are derived? a) Inference rule b) Module rule c) Both Inference & Module rule d) None of the mentioned Answer: a Explanation: Inference rule contains the standard pattern that leads to desired goal. The best form of inference rule is modus ponens.

16. Which is also called single inference rule? a) Reference b) Resolution c) Reform d) None of the mentioned Answer: b Explanation: Because resolution yields a complete inference rule when coupled with any search algorithm.

17. Which form is called as a conjunction of disjunction of literals? a) Conjunctive normal form b) Disjunctive normal form c) Normal form d) All of the mentioned Answer: a Explanation: None.

18. What can be viewed as a single lateral of disjunction? a) Multiple clause b) Combine clause c) Unit clause d) None of the mentioned Answer: c Explanation: A single literal can be viewed as a disjunction or one literal also, called a unit clause.

19. Which is a refutation complete inference procedure for propositional logic? a) Clauses b) Variables c) Propositional resolution d) Proposition Answer: c Explanation: Propositional resolution is a refutation complete inference procedure for propositional logic. Crack Job Placement Aptitude in First Attempt Prepare for Aptitude with 50+ Videos Lectures and Handmade Notes Click Here! 20. What kind of clauses are available in Conjunctive Normal Form? a) Disjunction of literals b) Disjunction of variables c) Conjunction of literals d) Conjunction of variables Answer: a Explanation: First-order resolution requires the clause to be in disjunction of literals in Conjunctive Normal Form.

21. What is the condition of literals in variables? a) Existentially quantified b) Universally quantified c) Quantified d) None of the mentioned Answer: b Explanation: Literals that contain variables are assumed to be universally quantified.

22. Which can be converted to inferred equivalent CNF sentence? a) Every sentence of propositional logic b) Every sentence of inference c) Every sentence of first-order logic d) All of the mentioned Answer: c Explanation: Every sentence of first-order logic can be converted to inferred equivalent CNF sentence.

23. Which sentence will be unsatisfiable if the CNF sentence is unsatisfiable? a) Search statement b) Reading statement c) Replaced statement d) Original statement Answer: d Explanation: The CNF statement will be unsatisfiable just when the original sentence is unsatisfiable.

24. Which rule is equal to the resolution rule of first-order clauses? a) Propositional resolution rule b) Inference rule c) Resolution rule d) None of the mentioned Answer: a Explanation: The resolution rule for first-order clauses is simply a lifted version of the propositional resolution rule.

25. At which state does the propositional literals are complementary? a) If one variable is less b) If one is the negation of the other c) All of the mentioned d) None of the mentioned Answer: b Explanation: Propositional literals are complementary if one is the negation of the other.

26. What is meant by factoring? a) Removal of redundant variable b) Removal of redundant literal c) Addition of redundant literal d) Addition of redundant variable Answer: b Explanation: None.

27. What will happen if two literals are identical? a) Remains the same b) Added as three c) Reduced to one d) None of the mentioned Answer: c Explanation: Propositional factoring reduces two literals to one if they are identical.

28. When the resolution is called as refutation-complete? a) Sentence is satisfiable b) Sentence is unsatisfiable c) Sentence remains the same d) None of the mentioned Answer: b Explanation: Resolution is refutation-complete, if a set of sentence is unsatisfiable, then resolution will always be able to derive a contradiction.

29. Which condition is used to cease the growth of forward chaining? a) Atomic sentences b) Complex sentences c) No further inference d) All of the mentioned Answer: c Explanation: Forward chain can grow by adding new atomic sentences until no further inference is made.

30. Which closely resembles propositional definite clause? a) Resolution b) Inference c) Conjunction d) First-order definite clauses Answer: d Explanation: Because they are disjunction of literals of which exactly one is positive.

31. What is the condition of variables in first-order literals? a) Existentially quantified b) Universally quantified c) Both Existentially & Universally quantified d) None of the mentioned Answer: b Explanation: First-order literals will accept variables only if they are universally quantified.

32. Which are more suitable normal form to be used with definite clause? a) Positive literal b) Negative literal c) Generalized modus ponens d) Neutral literal Answer: c Explanation: Definite clauses are a suitable normal form for use with generalized modus ponen.

33. Which will be the instance of the class datalog knowledge bases? a) Variables b) No function symbols c) First-order definite clauses d) None of the mentioned Answer: b Explanation: If the knowledge base contains no function symbols means, it is an instance of the class datalog knowledge base.

34. Which knowledge base is called as fixed point? a) First-order definite clause are similar to propositional forward chaining b) First-order definite clause are mismatch to propositional forward chaining c) All of the mentioned d) None of the mentioned Answer: a Explanation: Fixed point reached by forward chaining with first-order definiteclause are similar to those for propositional forward chaining.

35. How to eliminate the redundant rule matching attempts in the forward chaining? a) Decremental forward chaining b) Incremental forward chaining c) Data complexity d) None of the mentioned Answer: b Explanation: We can eliminate the redundant rule matching attempts in the forward chaining by using incremental forward chaining.

36. From where did the new fact inferred on new iteration is derived? a) Old fact b) Narrow fact c) New fact d) All of the mentioned Answer: c Explanation: None.

37. Which will solve the conjuncts of the rule so that the total cost is minimized? a) Constraint variable b) Conjunct ordering c) Data complexity d) All of the mentioned Answer: b Explanation: Conjunct ordering will find an ordering to solve the conjuncts of the rule premise so that the total cost is minimized.

38. How many possible sources of complexity are there in forward chaining? a) 1 b) 2 c) 3 d) 4 Answer: c Explanation: The three possible sources of complexity are an inner loop, algorithm rechecks every rule on every iteration, algorithm might generate many facts irrelevant to the goal.

39. Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned Answer: b Explanation: Backward chaining algorithm will work backward from the goal and it will chain the known facts that support the proof.

40. Which is mainly used for automated reasoning? a) Backward chaining b) Forward chaining c) Logic programming d) Parallel programming Answer: c Explanation: Logic programming is mainly used to check the working process of the system.

41. What will backward chaining algorithm will return? a) Additional statements b) Substitutes matching the query c) Logical statement d) All of the mentioned Answer: b Explanation: It will contains the list of goals containing a single element and returns the set of all substitutions satisfying the query.

42. How can be the goal is thought of in backward chaining algorithm? a) Queue b) List c) Vector d) Stack View Answer Answer: d Explanation: The goals can be thought of as stack and if all of them us satisfied means, then current branch of proof succeeds.

43. What is used in backward chaining algorithm? a) Conjuncts b) Substitution c) Composition of substitution d) None of the mentioned Answer: c Explanation: None.

44. Which algorithm are in more similar to backward chaining algorithm? a) Depth-first search algorithm b) Breadth-first search algorithm c) Hill-climbing search algorithm d) All of the mentioned Answer: a Explanation: It is depth-first search algorithm because its space requirements are linear in the size of the proof.

45. Which problem can frequently occur in backward chaining algorithm? a) Repeated states b) Incompleteness c) Complexity d) Both Repeated states & Incompleteness Answer: d Explanation: If there is any loop in the chain means, It will lead to incompleteness and repeated states.

46. How the logic programming can be constructed? a) Variables b) Expressing knowledge in a formal language c) Graph d) All of the mentioned Answer: b Explanation: Logic programming can be constructed by expressing knowledge in a formal expression and the problem can be solved by running inference process.

47. What form of negation does the prolog allows? a) Negation as failure b) Proposition c) Substitution d) Negation as success Answer: a Explanation: None.

48. Which is omitted in prolog unification algorithm? a) Variable check b) Occur check c) Proposition check d) Both Occur & Proposition check Answer: b Explanation: Occur check is omitted in prolog unification algorithm because of unsound inferences.

49. Knowledge and reasoning also play a crucial role in dealing with __________________ environment. a) Completely Observable b) Partially Observable c) Neither Completely nor Partially Observable d) Only Completely and Partially Observable Answer: b Explanation: Knowledge and reasoning could aid to reveal other factors that could complete environment.

50. Treatment chosen by doctor for a patient for a disease is based on _____________ a) Only current symptoms b) Current symptoms plus some knowledge from the textbooks c) Current symptoms plus some knowledge from the textbooks plus experience d) All of the mentioned Answer: c Explanation: None.

51. A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions. a) True b) False Answer: a Explanation: Refer definition of Knowledge based agents.

52. A) Knowledge base (KB) is consists of set of statements. B) Inference is deriving a new sentence from the KB. Choose the correct option. a) A is true, B is true b) A is false, B is false c) A is true, B is false d) A is false, B is true Answer: a Explanation: None.

53. Wumpus World is a classic problem, best example of _______ a) Single player Game b) Two player Game c) Reasoning with Knowledge d) Knowledge based Game Answer: c Explanation: Refer the definition of Wumpus World Problem.

54. ‘α |= β ‘(to mean that the sentence α entails the sentence β) if and only if, in every model in which α is _____ β is also _____ a) True, true b) True, false c) False, true d) False, false Answer: a Explanation: Refer the definition of law of entailment.

55. Which is not a property of representation of knowledge? a) Representational Verification b) Representational Adequacy c) Inferential Adequacy d) Inferential Efficiency Answer: a Explanation: None.

56. Which is not Familiar Connectives in First Order Logic? a) and b) iff c) or d) not Answer: d Explanation: “not” is coming under propositional logic and is therefore not a connective.

57. Inference algorithm is complete only if _____________ a) It can derive any sentence b) It can derive any sentence that is an entailed version c) It is truth preserving d) It can derive any sentence that is an entailed version & It is truth preserving

58. An inference algorithm that derives only entailed sentences is called sound or truth-preserving. a) True b) False Answer: a Explanation: None.

59. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable. a) True b) False Answer: a Explanation: Rule of universal instantiation.

60. The corresponding Existential Instantiation rule: for the existential quantifier is slightly more complicated. For any sentence a, variable v, and constant symbol k that does not appear elsewhere in the knowledge base. a) True b) False Answer: a Explanation: Rule of existential instantiation.

61. What among the following could the universal instantiation of ___________ For all x King(x) ^ Greedy(x) => Evil(x) a) King(John) ^ Greedy(John) => Evil(John) b) King(y) ^ Greedy(y) => Evil(y) c) King(Richard) ^ Greedy(Richard) => Evil(Richard) d) All of the mentioned Answer: d Explanation: Refer the definition if universal instantiation.

62. Lifted inference rules require finding substitutions that make different logical expressions looks identical. a) Existential Instantiation b) Universal Instantiation c) Unification d) Modus Ponen Answer: c Explanation: None.

63. Which of the following is not the style of inference? a) Forward Chaining b) Backward Chaining c) Resolution Refutation d) Modus Ponen Answer: d Explanation: Modus ponen is a rule for an inference.

64. In order to utilize generalized Modus Ponens, all sentences in the KB must be in the form of Horn sentences. a) True b) False Answer: a Explanation: None.

65. For resolution to apply, all sentences must be in conjunctive normal form, a conjunction of disjunctions of literals. a) True b) False Answer: a Explanation: None.

66. What are the two basic types of inferences? a) Reduction to propositional logic, Manipulate rules directly b) Reduction to propositional logic, Apply modus ponen c) Apply modus ponen, Manipulate rules directly d) Convert every rule to Horn Clause, Reduction to propositional logic Answer: a Explanation: None.

67. Which among the following could the Existential instantiation of ∃x Crown(x) ^ OnHead(x, Johnny)? a) Crown(John) ^ OnHead(John, Jonny) b) Crown(y) ^ OnHead(y, y, x) c) Crown(x) ^ OnHead(x, Jonny) d) None of the mentioned Answer: a Explanation: None.

68. Translate the following statement into FOL. “For every a, if a is a PhD student, then a has a master degree” a) ∀ a PhD(a) -> Master(a) b) ∃ a PhD(a) -> Master(a) c) A is true, B is true d) A is false, B is false Answer: a Explanation: None

69. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable. a) True b) False Answer: a Explanation: Rule of universal instantiation.

1. What is Fuzzy Logic? A. a method of reasoning that resembles human reasoning B. a method of question that resembles human answer C. a method of giving answer that resembles human answer. D. None of the Above View Answer Ans : A Explanation: Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning.

2. How many output Fuzzy Logic produce? A. 2 B. 3 C. 4 D. 5 View Answer Ans : A Explanation: The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.

3. Fuzzy Logic can be implemented in? A. Hardware B. software C. Both A and B D. None of the Above View Answer Ans : C Explanation: It can be implemented in hardware, software, or a combination of both.

4. The truth values of traditional set theory is ____________ and that of fuzzy set is __________ A. Either 0 or 1, between 0 & 1 B. Between 0 & 1, either 0 or 1 C. Between 0 & 1, between 0 & 1 D. Either 0 or 1, either 0 or 1 View Answer Ans : A Explanation: Refer the definition of Fuzzy set and Crisp set.

5. How many main parts are there in Fuzzy Logic Systems Architecture? A. 3 B. 4 C. 5 D. 6 View Answer Ans : B Explanation: It has four main parts.

6. Each element of X is mapped to a value between 0 and 1. It is called _____. A. membership value B. degree of membership C. membership value D. Both A and B View Answer Ans : D Explanation: each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership.

7. How many level of fuzzifier is there? A. 4 B. 5 C. 6 D. 7 View Answer Ans : B Explanation: There is 5 level to fuzzifier

8. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. A. AND B. OR C. NOT D. All of the above View Answer Ans : D Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

9. The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ A. Fuzzy Set B. Crisp Set C. Both A and B D. None of the Above View Answer Ans : A Explanation: Fuzzy logic deals with linguistic variables.

10. What action to take when IF (temperature=Warm) AND (target=Warm) THEN? A. Heat B. No_Change C. Cool D. None of the Above View Answer Ans : B Explanation: IF (temperature=Warm) AND (target=Warm) THEN No_change

11. What is the form of Fuzzy logic? a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic View Answer Answer: c Explanation: With fuzzy logic set membership is defined by certain value. Hence it could have many values to be in the set.

12. Traditional set theory is also known as Crisp Set theory. a) True b) False View Answer Answer: a Explanation: Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.

13. The truth values of traditional set theory is ____________ and that of fuzzy set is __________ a) Either 0 or 1, between 0 & 1 b) Between 0 & 1, either 0 or 1 c) Between 0 & 1, between 0 & 1 d) Either 0 or 1, either 0 or 1 View Answer Answer: a Explanation: Refer the definition of Fuzzy set and Crisp set.

14. Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. a) True b) False View Answer Answer: a Explanation: None.

15. The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ a) Fuzzy Set b) Crisp Set c) Fuzzy & Crisp Set d) None of the mentioned View Answer Answer: a Explanation: Fuzzy logic deals with linguistic variables.

16. The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities View Answer Answer: b Explanation: Both Probabilities and degree of truth ranges between 0 – 1.

17. Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai. a) True b) False View Answer Answer: a Explanation: None.

18. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. a) AND b) OR c) NOT d) All of the mentioned View Answer Answer: d Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

19. There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. a) Hedges b) Lingual Variable c) Fuzz Variable d) None of the mentioned View Answer Answer: a Explanation: None.

20. Fuzzy logic is usually represented as ___________ a) IF-THEN-ELSE rules b) IF-THEN rules c) Both IF-THEN-ELSE rules & IF-THEN rules d) None of the mentioned View Answer Answer: b Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices. Rules are usually expressed in the form: IF variable IS property THEN action

21. Like relational databases there does exists fuzzy relational databases. a) True b) False View Answer Answer: a Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova dissertation.

22. ______________ is/are the way/s to represent uncertainty. a) Fuzzy Logic b) Probability c) Entropy d) All of the mentioned View Answer Answer: d Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).

23. ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned View Answer Answer: c Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

24. Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous. a.) True b.) False Answer: A

25.The membership functions are generally represented in a.) Tabular form b) Graphical form c) Mathematical form d) Logical form Ans: B

26.Membership function can be thought of as a technique to solve empirical problems on the basis of a) knowledge b) example c) learning d) experience Ans: D

27.Three main basic features involved in characterizing membership function are a)Intution, Inference, Rank Ordering b)Fuzzy Algorithm, Neural network, Genetic Algorithm c)Core, Support , Boundary d)Weighted Average, center of Sums, Median Ans : C

28. A fuzzy set whose membership function has at least one element x in the universe whose membership value is unity is called a) sub normal fuzzy sets b) normal fuzzy set c) convex fuzzy set d) concave fuzzy set Ans: B

29. In a Fuzzy set a prototypical element has a value a) 1 b) 0 c) infinite d) not defined Ans: A

30. A fuzzy set wherein no membership function has its value equal to 1 is called a) Normal fuzzy set b) Sub normal fuzzy set c) convex fuzzy set d) non convex fuzzy set Ans: B

31.A fuzzy set has a membership function whose membership values are strictly monotonically increasing or strictly monotonically decreasing or strictly monotonically increasing than strictly monotonically decreasing with increasing values for elements in the universe a) Convex fuzzy set b) Concave fuzzy set c) Non Concave fuzzy set d) Non Convex fuzzy set Ans : A

1. Who was the inventor of the first neurocomputer? A. Dr. John Hecht-Nielsen B. Dr. Robert Hecht-Nielsen C. Dr. Alex Hecht-Nielsen D. Dr. Steve Hecht-Nielsen Ans : B Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.

2. How many types of Artificial Neural Networks? A. 2 B. 3 C. 4 D. 5 Ans : A Explanation: There are two Artificial Neural Network topologies : FeedForward and Feedback.

3. In which ANN, loops are allowed? A. FeedForward ANN B. FeedBack ANN C. Both A and B D. None of the Above Ans : B Explanation: FeedBack ANN loops are allowed. They are used in content addressable memories.

4. What is the full form of BN in Neural Networks? A. Bayesian Networks B. Belief Networks C. Bayes Nets D. All of the above Ans : D Explanation: The full form BN is Bayesian networks and Bayesian networks are also called Belief Networks or Bayes Nets.

5. What is the name of node which take binary values TRUE (T) and FALSE (F)? A. Dual Node B. Binary Node C. Two-way Node D. Ordered Node Ans : B Explanation: Boolean nodes : They represent propositions, taking binary values TRUE (T) and FALSE (F).

6. What is an auto-associative network? A. a neural network that contains no loops B. a neural network that contains feedback C. a neural network that has only one loop D. a single layer feed-forward neural network with pre-processing Ans : B Explanation: An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.

7. What is Neuro software? A. A software used to analyze neurons B. It is powerful and easy neural network C. Designed to aid experts in real world D. It is software used by Neurosurgeon Ans : B Explanation: Neuro software is powerful and easy neural network.

8. Neural Networks are complex ______________ with many parameters. A. Linear Functions B. Nonlinear Functions C. Discrete Functions D. Exponential Functions Ans : A Explanation: Neural networks are complex linear functions with many parameters.

9. Which of the following is not the promise of artificial neural network? A. It can explain result B. It can survive the failure of some nodes C. It has inherent parallelism D. It can handle noise Ans : A Explanation: The artificial Neural Network (ANN) cannot explain result.

10. The output at each node is called_____. A. node value B. Weight C. neurons D. axons Ans : A Explanation: The output at each node is called its activation or node value.

11.ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems. True False Ans : A

12:Artificial neural network used for A.Pattern Recognition B.Classification C.Clustering D.All of these Ans : D

13:A Neural Network can answer A.For Loop questions B.what-if questions C.IF-The-Else Analysis Questions D.None of these Ans : B

14:Ability to learn how to do tasks based on the data given for training or initial experience A.Self Organization B.Adaptive Learning C.Fault tolerance D.Robustness Ans : B

15: Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is A.Adaptive Learning B.Self Organization C.What-If Analysis D.Supervised Learniing Ans : B

16: In artificial Neural Network interconnected processing elements are called A.nodes or neurons B.weights C.axons D.Soma Ans:A

17: Each connection link in ANN is associated with ________ which has information about the input signal. A.neurons B.weights C.bias D.activation function Ans : B

18.Neurons or artificial neurons have the capability to model networks of original neurons as found in brain A.True B.False Ans : A

19.Internal state of neuron is called __________, is the function of the inputs the neurons receives A.Weight B.activation or activity level of neuron C.Bias D.None of these Ans: B

1. Which university introduced Expert systems ? A. Massachusetts Institute of Technology B. University of Oxford C. Stanford University D. University of Cambridge Ans : C Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department.

2. Which of the following is not a Capabilities of Expert Systems? A. Advising B. Demonstrating C. Explaining D. Expanding Ans : D Explanation: Expanding is not Capabilities of Expert Systems.

3. Which of the following are Components of Expert Systems? A. Knowledge Base B. Inference Engine C. User Interface D. All of the above Ans : D Explanation: The components of ES include : Knowledge Base, Inference Engine, User Interface.

4. Which of the following is incorrect application of Expert System? A. Design Domain B. Monitoring Systems C. Knowledge Domain D. Systems domain Ans : D Explanation: Systems domain is incorrect application of Expert System

5. In LISP, the function returns t if is even and nil otherwise ___________+ A. (evenp <integer>) B. (even <integer>) C. (numeven <integer>) D. (numnevenp <integer>) Ans : A Explanation: In LISP, the function returns t if <integer> is even and nil otherwise (evenp <integer>)

6. The “Turing Machine” showed that you could use a/an _____ system to program any algorithmic task. A. binary B. electro-chemical C. recursive D. semantic Ans : A Explanation: The “Turing Machine” showed that you could use a/an binary system to program any algorithmic task.

7. Input segments of AI programming contain(s)? A. Sound B. Smell C. Touch D. None of the Above Ans : D Explanation: Input segments of AI programming contain(s) sounds, smell, touch.

8. Which of the following is not a benefits of Expert Systems? A. Availability B. Speed C. Time D. Less Error Rate Ans : C Explanation: Time is not Benefits of Expert Systems.

9. What is the full form of JESS in Expert System Technology? A. Java Expert System Shell B. Javascript Expert System Shell C. Java Expert Sub System D. Javascript Expert Sub System Ans : A Explanation: Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.

10. What is the form of Knowledge representation? A. IF-THEN B. IF-THEN-ELSE C. IF-ELSE D. All of the above Ans : B Explanation: It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.

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• Artificial Intelligence MCQs
• Intelligent Agents
• Natural Language Processing
• Problem Solving Agents
• Partial Order Planning
• Expert System
• Fuzzy Logics
• Neural Networks
• Rule Based Systems
• Semantic Networks
• Bayesian Networks
• Alpha Beta Pruning
• Text Mining
• Reinforcement Learning
• Human Computer Interaction
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• Top 5 AI Courses
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• Best Python Books

Artificial Intelligence Agents MCQ

Intelligent Agents MCQs : This section focuses on "Agents" in Artificial Intelligence. These Multiple Choice Questions (MCQ) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.

A. agent B. environment C. Both A and B D. None of the Above

Explanation: An AI system is composed of an agent and its environment.

A. An agent is anything that can perceive its environment through sensors B. An agent is anything that can change its environment through sensors C. An agent is anything that can control its environment through sensors D. None of the Above

Explanation: An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors

A. Sensors and Actuators B. Sensors C. Perceiver D. Perceiver and Sensor

Explanation: An agent is anything that can be viewed as perceiving and acting upon the environment through the sensors and actuators.

A. 1 B. 2 C. 3 D. 4

Explanation: There are 4 types of agents in artificial intelligence

A. Model based B. Utility based C. Simple reflex D. target based

Explanation: The four types of agents are Simple reflex, Model based, Goal based and Utility based agents.

A. Behavior of Agent B. Percept C. Percept Sequence D. Agent Function

Explanation: Percept is agent's perceptual inputs at a given instance.

A. Perceiving B. Observing C. Learning D. Sequence

Explanation: An agent can improve its performance by storing its previous actions.

A. being reasonable B. being sensible C. having good sense of judgment. D. All of the above

Explanation: Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment.

A. performance measures B. Percept Sequence C. reaction D. actions

Explanation: Rationality of an agent does not depends on reaction

A. Perceptual Measure, Environment, Actuators, and Sensors B. Performance Measure, Environment, Actuators, and Sensors C. Performance Measure, Entity, Actuators, and Sensors D. Performance Measure, Environment, Agent Function, and Sensors

Explanation: The problem the agent solves is characterized by Performance Measure, Environment, Actuators, and Sensors (PEAS).

A. Architecture B. Agent Program C. Architecture + Agent Program D. None of the Above

Explanation: Agent's structure can be viewed as - Agent = Architecture + Agent Program

A. Discrete / Continuous B. Static / Dynamic C. Deterministic / Non-deterministic D. No agent / Multiple agents

Explanation: No agent / Multiple agents is not Properties of Environment.

A. Problem B. Solution C. Agent D. Observation

Explanation: Task environments will pose a problem and rational agent will find the solution for the posed problem.

A. Discrete B. Continuous C. Episodic D. Non-deterministic

Explanation: chess is example of Discrete Properties of Environment

A. Dynamic B. Static C. Semi Dynamic D. Continuous

Explanation: As the problem in crossword puzzle are posed at beginning itself, So it is static.

A. Environment does not change with the passage of time B. Agent performance changes C. Environment does not change with the passage of time, but Agent performance changes D. Environment will be changed

Explanation: If the environment does not change with the passage of time, but the agent performance changes by time.

A. Computers in space and earth B. Image categorization techniques C. Statistical data on image pixel intensity value and histograms D. All of the above

Explanation: An environment is something which agent stays in.

A. Sensors B. MEMS C. Actuators D. Agent Function

Explanation: Micro ElectroMechanical System uses miniaturized accelerometers and gyroscopes and is used to produce actuators.

A. Relaxed B. Relational C. Both A and B D. None of the Above

Explanation: Because an agent may experience any kind of situation, So that an agent should use all kinds of architecture.

A. Decision-making algorithm B. conditional algortihm C. Complex algorithm D. None of the Above

Explanation: Decision-making and learning algorithms can operate over the joint state space and thereby serve to implement and used to improve the computational activities.

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