AD-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
   aDefine Artificial Intelligence and explain its motivation and need.
   How to answer

   • Definition of Artificial Intelligence as intelligent agents/systems mimicking human intelligence
   • Motivation: automation, handling complexity, large data, hazardous tasks
   • Need: reasoning, learning, perception, natural language, decision making
   • Examples of AI need in modern applications
2. 7 Marks
   bDiscuss goals of AI and its contribution to modern technology.
   How to answer

   • Goals of AI: reasoning, knowledge representation, learning, perception, NLP, robotics
   • Contribution to healthcare, education, industry, automation
   • Contribution to modern technology: ML, expert systems, computer vision
   • Future impact and conclusion
3. 7 Marks
   aExplain production systems and their characteristics.
   How to answer

   • Definition of production system: global database, production rules, control system
   • Characteristics: modularity, modifiability, opacity, etc.
   • Types: monotonic, non-monotonic, commutative systems
   • Example of production rule
4. 7 Marks
   bDescribe search space and hill climbing search technique.
   How to answer

   • Definition of search space / state space
   • Hill climbing procedure and steps
   • Types, advantages and limitations like local maxima, plateau
   • Example illustration
5. 7 Marks
   aExplain heuristic search techniques with examples.
   How to answer

   • Definition of heuristic and heuristic search
   • Techniques: generate-and-test, best-first, hill climbing, etc.
   • Examples with heuristic function values
   • Advantages and limitations
6. 7 Marks
   bDiscuss A* and AO* search algorithms.
   How to answer

   • A* algorithm with f(n)=g(n)+h(n) and admissibility
   • Steps and example of A* search
   • AO* algorithm for AND-OR graphs
   • Comparison and applications
7. 7 Marks
   aExplain knowledge representation issues.
   How to answer

   • Need for knowledge representation
   • Issues: adequacy, granularity, reasoning efficiency
   • Representational vs inferential adequacy
   • Acquisition and incompleteness issues
8. 7 Marks
   bCompare propositional logic and predicate logic.
   How to answer

   • Syntax and semantics of propositional logic
   • Syntax and semantics of predicate logic with quantifiers
   • Comparison on expressiveness, reasoning power
   • Examples of each
9. 7 Marks
   aExplain resolution and inference mechanisms.
   How to answer

   • Definition of resolution principle and inference
   • Steps of resolution in propositional/predicate logic
   • Forward and backward chaining mechanisms
   • Example of resolution refutation
10. 7 Marks
    bDiscuss monotonic and non-monotonic reasoning.
    How to answer

    • Definition of monotonic reasoning with properties
    • Definition of non-monotonic reasoning with need
    • Differences between the two
    • Examples and applications
11. 7 Marks
    aExplain probabilistic reasoning and Bayes' theorem.
    How to answer

    • Concept of probabilistic reasoning under uncertainty
    • Bayes' theorem statement $P(H|E)=P(E|H)P(H)/P(E)$
    • Components: prior, likelihood, posterior
    • Example application in AI
12. 7 Marks
    bDescribe semantic networks, frames and scripts.
    How to answer

    • Structure of semantic networks with nodes and links
    • Frames: slots, fillers, inheritance
    • Scripts: scenes, roles, props with example
    • Comparison and uses in knowledge representation
13. 7 Marks
    aExplain minimax algorithm and alpha-beta pruning.
    How to answer

    • Minimax principle for two-player games with max-min steps
    • Game tree evaluation procedure
    • Alpha-beta pruning conditions and cut-offs
    • Advantages in reducing search complexity
14. 7 Marks
    bDiscuss block world problem in robotics and planning.
    How to answer

    • Description of block world domain and operators
    • Representation of states and goals
    • Role in robotics and planning research
    • Planning methods like STRIPS example
15. 7 Marks
    aExplain expert systems and their characteristics.
    How to answer

    • Definition of expert system
    • Characteristics: high performance, explainability, reliability
    • Difference from conventional programs
    • Examples like MYCIN, DENDRAL
16. 7 Marks
    bDiscuss components, inference engine and limitations of expert systems.
    How to answer

    • Components: knowledge base, working memory, user interface
    • Working of inference engine with chaining
    • Limitations: knowledge acquisition bottleneck, narrow domain
    • Applications and conclusion