2020 question paper
Artificial intelligence
24 questions
Q1a. Parts of speech tagging determines (i) parts of speech for each word dynamically as per meaning of the sentence (ii) parts of speech for each word dynamically as per sentence structure (iii) all parts of speech for a specific word given as input (iv) All of the above20202m
Module 4: Advanced AI Applications
View this question on its own page →Parts of speech tagging determines
(i) parts of speech for each word dynamically as per meaning of the sentence
(ii) parts of speech for each word dynamically as per sentence structure
(iii) all parts of speech for a specific word given as input
(iv) All of the aboveQ1b. Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by (i) fuzzy logic (ii) shallow semantic analysis (iii) word sense disambiguation (iv) All of the above20202m
Module 4: Advanced AI Applications
View this question on its own page →Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by
(i) fuzzy logic
(ii) shallow semantic analysis
(iii) word sense disambiguation
(iv) All of the aboveQ1c. Decision support programs are designed to help managers make (i) budget projections (ii) visual presentations (iii) business decisions (iv) vacation schedules20202m
Module 4: Advanced AI Applications
View this question on its own page →Decision support programs are designed to help managers make
(i) budget projections
(ii) visual presentations
(iii) business decisions
(iv) vacation schedulesQ1d. Which is the best way to go for game playing problem? (i) Linear approach (ii) Heuristic approach (some knowledges are stored) (iii) Random approach (iv) An optimal approach20202m
Module 1: Introduction and Search Techniques
View this question on its own page →Which is the best way to go for game playing problem?
(i) Linear approach
(ii) Heuristic approach (some knowledges are stored)
(iii) Random approach
(iv) An optimal approachQ1e. What are not represented by using propositional logic? (i) Objects (ii) Relations (iii) Both objects and relations (iv) None of the above20202m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →What are not represented by using propositional logic?
(i) Objects
(ii) Relations
(iii) Both objects and relations
(iv) None of the aboveQ1f. A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions. (i) True (ii) False20202m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions.
(i) True
(ii) FalseQ1h. What are the two basic types of inference? (i) Reduction to propositional logic, manipulate rules directly (ii) Reduction to propositional logic, apply modus ponen (iii) Apply modus ponen, manipulate rules directly (iv) Convert every rule of horn clause, reduction to propositional logic20202m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →What are the two basic types of inference?
(i) Reduction to propositional logic, manipulate rules directly
(ii) Reduction to propositional logic, apply modus ponen
(iii) Apply modus ponen, manipulate rules directly
(iv) Convert every rule of horn clause, reduction to propositional logicQ1i. What are the main components of the expert systems? (i) Inference engine (ii) Knowledge base (iii) Both inference engine and knowledge base (iv) None of the above20202m
Module 4: Advanced AI Applications
View this question on its own page →What are the main components of the expert systems?
(i) Inference engine
(ii) Knowledge base
(iii) Both inference engine and knowledge base
(iv) None of the aboveQ2a. Explain with diagram the organization of a natural language understanding system.20207m
Module 4: Advanced AI Applications
View this question on its own page →Explain with diagram the organization of a natural language understanding system.
Q2b. Describe all the levels of language understanding in natural language processing system.20207m
Module 4: Advanced AI Applications
View this question on its own page →Describe all the levels of language understanding in natural language processing system.
Q3a. What are agents in AI? How do agents work to import intelligence to a system? Classify the different types of agents and briefly discuss their properties.20207m
Module 1: Introduction and Search Techniques
View this question on its own page →What are agents in AI? How do agents work to import intelligence to a system? Classify the different types of agents and briefly discuss their properties.
Q3b. Draw the semantic network of the following sentence: Kavita gives a book to her friend.20207m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Draw the semantic network of the following sentence:
Kavita gives a book to her friend.
Q5a. Explain the procedure of knowledge acquisition with the help of a diagram.20207m
Module 4: Advanced AI Applications
View this question on its own page →Explain the procedure of knowledge acquisition with the help of a diagram.
Q5b. What is First Order Predicate Logic (FOPL)? Represent the following facts in FOPL: "Anyone passing his AI paper and getting an opportunity to work on live project is Happy. But anyone who studies sincerely or is Lucky can pass all his exams. Ramu did not study but he is Lucky. Anyone who is Lucky gets a live project to work."20207m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →What is First Order Predicate Logic (FOPL)? Represent the following facts in FOPL:
"Anyone passing his AI paper and getting an opportunity to work on live project is Happy. But anyone who studies sincerely or is Lucky can pass all his exams. Ramu did not study but he is Lucky. Anyone who is Lucky gets a live project to work."
Q6a. Write short notes on the following: Knowledge20203.5m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Write short notes on the following:
Knowledge
Q6b. Write short notes on the following: Intelligence20203.5m
Module 1: Introduction and Search Techniques
View this question on its own page →Write short notes on the following:
Intelligence
Q6c. Write short notes on the following: Inheritance knowledge20203.5m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Write short notes on the following:
Inheritance knowledge
Q6d. Write short notes on the following: Knowledge management20203.5m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Write short notes on the following:
Knowledge management
Q7a. Describe the following with suitable examples: Logistic regression20207m
Module 3: Machine Learning Fundamentals
View this question on its own page →Describe the following with suitable examples:
Logistic regression
Q7b. Describe the following with suitable examples: Back propagation algorithm20207m
Module 3: Machine Learning Fundamentals
View this question on its own page →Describe the following with suitable examples:
Back propagation algorithm
Q8a. Differentiate between forward chaining and backward chaining. On what factors does the decision to choose forward or backward chaining depend?20207m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Differentiate between forward chaining and backward chaining. On what factors does the decision to choose forward or backward chaining depend?
Q8b. Consider the following sentences: * John likes all kinds of food. * Apples are food. * Chicken is food. * Anything anyone eats and isn't killed by it, is food. * Sue eats everything Bill eats. (i) Translate the sentences into formulae in predicate logic. Prove that John likes peanuts using backward chaining. (ii) Convert the formulae of part (i) into clause form. (iii) Prove that John likes peanuts using resolution. (iv) Use resolution to answer the question, "What food does Sue eat?"20207m
Module 2: Knowledge Representation and Reasoning
View this question on its own page →Consider the following sentences:
- John likes all kinds of food.
- Apples are food.
- Chicken is food.
- Anything anyone eats and isn't killed by it, is food.
- Sue eats everything Bill eats.
(i) Translate the sentences into formulae in predicate logic. Prove that John likes peanuts using backward chaining.
(ii) Convert the formulae of part (i) into clause form.
(iii) Prove that John likes peanuts using resolution.
(iv) Use resolution to answer the question, "What food does Sue eat?"Q9a. What is the Turing test? If the machine passes the Turing test, does it mean that the system is intelligent? What are the associated problems with Turing test? What are required improvement/advances to overcome these problems?20207m
Module 1: Introduction and Search Techniques
View this question on its own page →What is the Turing test? If the machine passes the Turing test, does it mean that the system is intelligent? What are the associated problems with Turing test? What are required improvement/advances to overcome these problems?
Q9b. What is the goal of the support vector machine (SVM)? How to compute the margin?20207m
Module 3: Machine Learning Fundamentals
View this question on its own page →What is the goal of the support vector machine (SVM)? How to compute the margin?