IT-504-A Artificial Intelligence - Jun 2025 Question Paper

Under each question, How to answer lists the points an answer should cover, outlined by CampusPrep from the syllabus. Check the details against your textbook.

1. 7 Marks
   aExplain the terms $f'$, $g'$, $h'$, open and closed in $A^*$ algorithm with suitable example.
   How to answer

   • Define $f'=g'+h'$ with $g'$ cost so far and $h'$ heuristic estimate
   • Define OPEN list and CLOSED list and their roles
   • Outline A* selection and expansion steps
   • Illustrate with small graph example showing f' calculation
2. 7 Marks
   bTest whether the following production systems are commutative. Justify your answer.

   1. i)Knowledge Base If A & B then C If C then D. If A & D then E. Initial wm = {A, B}
   2. ii)Knowledge Base If A & B then C. if X & Y then C. if A then E if B then F Initial wm = {A, B, X, Y}
   How to answer

   • State conditions for commutative production system
   • Simulate system i) from {A,B} to check order independence
   • Simulate system ii) from {A,B,X,Y} to check order independence
   • Conclude commutative / non-commutative with justification
3. 7 Marks
   aAssume the following facts: i) Steve only likes easy course ii) Science courses are hard iii) All the courses in the CSE department are easy iv) CS301 is a CSE course Use resolution to answer the question "what course would steve like"?
   How to answer

   • Convert facts i)-iv) to predicate logic / clausal form
   • Negate goal and add to clause set
   • Apply resolution steps with unification
   • Derive answer binding showing Steve likes CS301
4. 7 Marks
   bShow that the following formula is valid. $(A(X) \lor B(Y)) \to C(Z) \Rightarrow (\neg A(X) \land \neg B(Y)) \lor C(Z)$ Where $X, Y, Z$ are variables and $A, B$ and $C$ are predicators.
   How to answer

   • Rewrite implication $(A(X) \lor B(Y)) \to C(Z)$ as disjunction
   • Apply De Morgan and implication equivalence
   • Show equivalence to $(\neg A(X) \land \neg B(Y)) \lor C(Z)$
   • Conclude formula is valid
5. 7 Marks
   aShow a conceptual dependency representation of the sentence. "John begged Mary for a pencil" How does this representation make it possible to the answer the question - "Did John talk to Mary"?
   How to answer

   • Give CD primitives for MTRANS, SPEAK, WANT involved in begging
   • Draw CD graph for John begged Mary for a pencil
   • Explain inference that begging requires speaking
   • Answer Did John talk to Mary as yes
6. 7 Marks
   bConstruct partitioned semantic net representations for following:

   1. i)Every batter hit a ball.
   2. ii)All the batters like the pitcher.
   How to answer

   • Explain partitioned semantic net for quantifiers and events
   • Draw partition for Every batter hit a ball
   • Draw partition for All the batters like the pitcher
   • Show ISA, instance and relation arcs
7. 7 Marks
   aFor each of the sentences, show a parse tree.

   1. i)John wanted to go to the movie with Sue.
   2. ii)I heard the story listening to the radio.
   How to answer

   • Give grammar rules for NP, VP, PP used
   • Draw parse tree for John wanted to go to the movie with Sue
   • Draw parse tree for I heard the story listening to the radio
   • Show attachment ambiguity if applicable
8. 7 Marks
   bShow the computation for the first 3 ply moves in a tac-tac-toe game using $\alpha$-$\beta$ cut off algorithm.
   How to answer

   • Define alpha-beta bounds and ply concept
   • Expand game tree for first 3 ply of tic-tac-toe
   • Show alpha and beta updates and cut-offs
   • Give best move from computation
9. 7 Marks
   aHow learning in problem solving improve the performance?
   How to answer

   • Define learning in problem solving agents
   • Explain rote, analogy and discovery effects
   • Show reduction of search and improved heuristics
   • Give example of performance improvement
10. 7 Marks
    bCase based reasoning shares many of the same ideas of learning by analogy. Briefly describe how transformational and derivational analogy could apply in case based reasoning systems.
    How to answer

    • Define case based reasoning and analogy learning
    • Describe transformational analogy - adapting past solution
    • Describe derivational analogy - replaying reasoning steps
    • Compare their use in CBR systems
11. 7 Marks
    aConsider the following knowledge base: i) The-humidity-is-high $\lor$ the sky-is-cloudy ii) If the-sky-is-cloudy then it-will-rain iii) If the-humidity-is-high then it-is-hot. iv) it-is-not-hot and the goal : it-will-rain. Prove by resolution theorem that the goal is derivable from the knowledge base.
    How to answer

    • Convert i)-iv) to clausal form
    • Negate goal it-will-rain and add clause
    • Apply resolution steps to derive empty clause
    • Conclude goal is derivable
12. 7 Marks
    bWhat are the different stages of the expert system life cycles? Describe each stage.
    How to answer

    • List life cycle stages: identification, conceptualization, formalization, implementation, testing
    • Describe purpose of each stage
    • Mention roles of expert and knowledge engineer
    • Note iteration and maintenance
13. 7 Marks
    aA math teacher gave her class two tests. 25% of the class passed both test and 42% of the class passed first test. Using Bayesian theorem calculate the % of those who passed first test also passed in second test.
    How to answer

    • State Bayes theorem $P(A|B)=P(B|A)P(A)/P(B)$
    • Identify $P(\text{both})=0.25$ and $P(\text{first})=0.42$
    • Compute $0.25/0.42 \approx 0.595$
    • Give percentage ~59.5%
14. 7 Marks
    bDefine fuzzy logic with its various operations.
    How to answer

    • Define fuzzy set and membership degree
    • Define complement, union, intersection operations
    • Give formulas with min, max and $1-\mu$
    • Give example of fuzzy operations
15. 14 Marks
    Write short notes on following (any two):

    1. i)Forward and Backward Reasoning
    2. ii)AO* algorithm
    3. iii)Refutation and Resolution
    How to answer

    • Explain forward vs backward reasoning with process
    • Outline AO* algorithm steps for AND-OR graphs
    • Explain refutation and resolution principle
    • Give examples and applications for any two