AL-304 Artificial Intelligence - Dec 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 scope of Artificial Intelligence with real-life applications.
   How to answer

   • Define AI and its scope/domains
   • List major areas: search, knowledge representation, ML, NLP, vision, robotics, expert systems
   • Give real-life applications with examples
   • Conclude with impact
2. 7 Marks
   bDiscuss the need and motivation of AI in modern computing.
   How to answer

   • Define modern computing challenges
   • Explain need for AI: automation, data volume, decision making
   • Discuss motivation: intelligence, learning, efficiency
   • Give examples in modern computing
3. 7 Marks
   aDefine search space in AI.
   How to answer

   • Define search space as set of all possible states/solutions
   • Explain components: initial state, goal state, operators
   • Relate to state-space search in AI
   • Give simple example
4. 7 Marks
   bExplain heuristic search strategy with suitable examples.
   How to answer

   • Define heuristic and heuristic function
   • Explain heuristic search strategy and informed search
   • Describe examples e.g., A*, best-first, hill climbing
   • Discuss advantages with example trace
5. 7 Marks
   aExplain characteristics of production systems.
   How to answer

   • Define production system: rules, database, control
   • List characteristics: modularity, modifiability, heuristic control
   • Explain control strategy and conflict resolution
   • Give example
6. 7 Marks
   bDifferentiate between hill climbing and best-first search techniques.
   How to answer

   • Define hill climbing: local search, greedy ascent
   • Define best-first search: heuristic OPEN list
   • Compare on basis: strategy, backtracking, optimality, completeness
   • Summarize in table
7. 7 Marks
   aExplain knowledge representation using predicate logic.
   How to answer

   • Define knowledge representation and predicate logic
   • Explain predicates, quantifiers, connectives
   • Show representation of facts and rules with examples
   • Discuss advantages/limitations
8. 7 Marks
   bDiscuss theorem proving and refutation techniques.
   How to answer

   • Define theorem proving in logic
   • Explain direct proof and refutation/resolution principle
   • Describe steps: CNF, unification, resolution
   • Give example of refutation
9. 7 Marks
   aExplain inference mechanisms in Artificial Intelligence.
   How to answer

   • Define inference and inference engine
   • Explain mechanisms: deduction, induction, abduction
   • Describe forward/backward chaining, resolution
   • Give example
10. 7 Marks
    bDifferentiate between monotonic and non-monotonic reasoning.
    How to answer

    • Define monotonic reasoning with property
    • Define non-monotonic reasoning with retraction
    • Compare on truth maintenance, assumptions, examples
    • Summarize in table
11. 7 Marks
    aExplain probabilistic reasoning with suitable examples.
    How to answer

    • Define uncertainty and probabilistic reasoning
    • Explain Bayes theorem and conditional probability
    • Give examples e.g., medical diagnosis
    • Discuss Bayesian networks briefly
12. 7 Marks
    bDiscuss forward reasoning and backward reasoning.
    How to answer

    • Define forward chaining: data-driven
    • Define backward chaining: goal-driven
    • Explain working with example trace
    • Compare applications
13. 7 Marks
    aExplain game playing in AI using minimax algorithm.
    How to answer

    • Define game playing and game tree
    • Explain minimax: max and min levels, evaluation
    • Describe algorithm steps with example game tree
    • Discuss limitations/complexity
14. 7 Marks
    bDiscuss alpha-beta cut-off and its advantages.
    How to answer

    • Define alpha-beta pruning concept
    • Explain alpha and beta cut-offs with example
    • Describe algorithm steps
    • List advantages: reduced nodes, same result, efficiency
15. 7 Marks
    aExplain expert system development environment.
    How to answer

    • Define expert system and development environment
    • Explain components: knowledge base, inference engine, tools
    • Describe roles: knowledge engineer, shells
    • Give example environment
16. 7 Marks
    bDiscuss benefits, limitations and applications of expert systems.
    How to answer

    • Define expert systems briefly
    • List benefits: consistency, availability, expertise preservation
    • List limitations: narrow domain, no common sense, cost
    • List applications: medicine, finance, engineering