AD-304 Artificial Intelligence - Nov 2022 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
   aWhat do you understand by the term "Intelligence"? Explain the goals of AI in modern technology.
   How to answer

   • Define intelligence (ability to acquire, understand, and apply knowledge).
   • Define artificial intelligence.
   • Enumerate primary goals: create expert systems, implement human intelligence in machines.
   • Discuss modern technology goals: automation, problem solving, learning, decision making.
2. 7 Marks
   bWrite Heuristic search algorithm. Explain it with suitable example.
   How to answer

   • Define heuristic function $h(n)$ and informed search.
   • Step-by-step algorithm (e.g., Best-First Search with OPEN and CLOSED lists).
   • Provide an illustrative search tree or graph example with heuristic values.
   • Explain path expansion and termination condition.
3. 7 Marks
   aExplain about A* algorithm in detail.
   How to answer

   • Define evaluation function: $f(n) = g(n) + h(n)$.
   • Explain meaning of $g(n)$ (cost so far) and $h(n)$ (estimated cost to goal).
   • Algorithm steps using OPEN and CLOSED sets.
   • Discuss properties: completeness, admissibility, and optimality.
4. 7 Marks
   bDifferentiate between Forward and Backward reasoning.
   How to answer

   • Definition of forward reasoning (data-driven) and backward reasoning (goal-driven).
   • Tabular comparison based on: approach, starting point, goal verification, and applicability.
   • Examples of each reasoning technique.
5. 7 Marks
   aExplain these terms: i) Frames ii) Scripts iii) Conceptual dependency
   How to answer

   • Frames: slots, fillers, default values, and inheritance.
   • Scripts: components (props, roles, entry conditions, scenes, results).
   • Conceptual Dependency (CD): Schank's theory, primitive ACTs, conceptual categories.
6. 7 Marks
   bElucidate various knowledge level representations involved in reasoning process.
   How to answer

   • Explain Newell's Knowledge Level hypothesis.
   • Describe levels: Knowledge level, Symbol/Logical level, Implementation level.
   • Discuss representation schemes used in reasoning (Propositional, First-Order Logic, Semantic networks, Production rules).
   • Relation between knowledge representation and inference mechanisms.
7. 7 Marks
   aExplain resolution in predicate logic.
   How to answer

   • Define resolution refutation in Predicate Logic (FOL).
   • Steps for converting WFF to Clause Form (Skolemization, dropping quantifiers, CNF).
   • Explain the unification algorithm and substitution.
   • Show a simple resolution step between two clauses.
8. 7 Marks
   bWrite about Natural Language Processing. Also explain about applications of NLP to design expert system.
   How to answer

   • Define NLP and its core phases (morphological, syntactic, semantic, pragmatic).
   • Describe role of NLP in expert system interfaces.
   • Applications: user query understanding, knowledge acquisition, explanation generation.
9. 7 Marks
   aExplain the alpha-beta cut-off in game playing techniques.
   How to answer

   • Briefly explain Minimax algorithm limitations.
   • Define Alpha and Beta values.
   • Explain conditions for Alpha cut-off and Beta cut-off.
   • Provide a small game tree illustrating pruned branches.
10. 7 Marks
    bExplain Bayes's theorem.
    How to answer

    • State Bayes' Theorem formula: $P(A|B) = \frac{P(B|A)P(A)}{P(B)}$.
    • Define terms: prior probability, posterior probability, likelihood, marginal likelihood.
    • Derive formula from conditional probability definitions.
    • Explain significance in probabilistic reasoning in AI.
11. 7 Marks
    aExplain about resolution, refutation and deduction.
    How to answer

    • Define Deduction (deriving logical conclusions from known premises).
    • Define Resolution (a rule of inference yielding contradiction).
    • Define Refutation (proof by contradiction: assuming $\neg P$ and deriving false/empty clause).
    • Explain how the three concepts combine in automated theorem proving.
12. 7 Marks
    bWrite about the semantic networks used in artificial intelligence.
    How to answer

    • Define semantic networks (directed graph of nodes and labeled edges).
    • Describe relationships: IS-A, HAS-A, instance-of.
    • Explain property inheritance.
    • Provide an illustrative diagram with an example domain (e.g., animal/mammal hierarchy).
13. 7 Marks
    aExplain about the contribution of AI in human life.
    How to answer

    • Overview of AI's transformative impact on daily life.
    • Healthcare (medical imaging, diagnosis, robotic surgery).
    • Transportation (autonomous vehicles, traffic routing).
    • Smart assistants, banking/finance (fraud detection), and education.
14. 7 Marks
    bExplain about forward and backward chaining.
    How to answer

    • Definition and process of forward chaining (data-driven, bottom-up).
    • Definition and process of backward chaining (goal-driven, top-down).
    • Application context for each (e.g., diagnostic vs planning systems).
    • Example rule set showing execution direction.
15. 7 Marks
    aWhat is Expert System? Write about its characteristics.
    How to answer

    • Definition of Expert System (computer system emulating human expert decision-making).
    • Key characteristics: high performance, understandability, reliability, responsiveness.
    • Separation of domain knowledge from inference mechanism.
16. 7 Marks
    bExplain about the components and capability of Expert system.
    How to answer

    • Block diagram of Expert System architecture.
    • Detail components: Knowledge Base, Inference Engine, User Interface, Explanation System, Knowledge Acquisition facility.
    • List capabilities: advising, explaining reasoning, solving complex domain-specific problems.