IT-504-A 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. Explain the goals and applications of Artificial Intelligence in detail. Discuss different definitions of AI given by various researchers.
   How to answer

   • Definition of Artificial Intelligence as simulation of human intelligence by machines
   • Goals of AI: reasoning, knowledge representation, learning, perception, NLP, problem solving
   • Major applications: expert systems, robotics, NLP, vision, games, healthcare etc.
   • Different researcher definitions: Turing, McCarthy, Minsky, Russell & Norvig etc.
2. 7 Marks
   bCompare and contrast the following search techniques: Explain their advantages, disadvantages, and applications.

   1. i)Breadth First Search and Best First Search
   2. ii)Hill Climbing and A* Algorithm
   How to answer

   • Principle of BFS vs Best First Search and Hill Climbing vs A* Algorithm
   • Comparison table on strategy, heuristic use, completeness, optimality
   • Advantages and disadvantages of each technique
   • Typical applications of each search method
3. 7 Marks
   aExplain Knowledge Representation using Predicate Logic (First Order Logic). Discuss constants, variables, predicates, quantifiers, and inference rules with suitable examples.
   How to answer

   • Definition of First Order Predicate Logic for knowledge representation
   • Constants, variables, predicates, functions with examples
   • Universal and existential quantifiers with examples
   • Inference rules: modus ponens, resolution, unification
4. 7 Marks
   bDescribe the role of logic in Artificial Intelligence. Explain how propositional and predicate logic support knowledge representation and automated reasoning.
   How to answer

   • Role of logic as formal basis for representation and reasoning
   • Propositional logic: syntax, semantics and limitations
   • Predicate logic: expressive power for objects and relations
   • How both support automated reasoning and inference
5. 7 Marks
   aState and explain Bayes' Theorem. Describe its significance in Artificial Intelligence. Illustrate the theorem with a suitable numerical example.
   How to answer

   • Statement of Bayes' Theorem: $P(H|E)=P(E|H)P(H)/P(E)$
   • Explanation of prior, likelihood, posterior terms
   • Significance in AI: reasoning under uncertainty, classification, diagnosis
   • Numerical example illustrating calculation
6. 7 Marks
   bWhat is probabilistic reasoning? Compare probabilistic reasoning with logical reasoning. Explain situations where probabilistic reasoning is preferred over deterministic approaches.
   How to answer

   • Definition of probabilistic reasoning for uncertain knowledge
   • Comparison with logical/deterministic reasoning on truth values and uncertainty
   • Situations preferring probabilistic: noisy, incomplete, medical diagnosis etc.
   • Examples of Bayesian networks and applications
7. 7 Marks
   aExplain different approaches to Natural Language Processing. Discuss rule-based, statistical, and hybrid approaches with suitable examples.
   How to answer

   • Overview of NLP approaches
   • Rule-based approach with grammar rules and example
   • Statistical / ML approach with corpora and probabilities
   • Hybrid approach combining rules and statistics
8. 7 Marks
   bExplain the Block World problem in robotics. Describe the representation of blocks, actions, and constraints. Explain how planning is used to solve the Block World problem.
   How to answer

   • Description of Block World domain in robotics planning
   • Representation: predicates for On, Clear, actions Pickup, Putdown, Stack
   • Constraints and initial/goal states
   • How STRIPS-like planning solves stacking goals
9. 7 Marks
   aExplain the architecture of artificial neural networks. Discuss single-layer and multi-layer neural networks with neat diagrams.
   How to answer

   • Basic neuron model: inputs, weights, activation, output
   • Single-layer architecture diagram and characteristics/limitations
   • Multi-layer architecture diagram with hidden layers
   • Comparison and applications
10. 7 Marks
    bExplain the development of expert systems. Discuss knowledge acquisition and knowledge representation techniques used in expert systems.
    How to answer

    • Stages of expert system development: identification to maintenance
    • Knowledge acquisition: elicitation from experts, sources
    • Knowledge representation: rules, frames, semantic nets
    • Example architecture: knowledge base, inference engine
11. 7 Marks
    aWhat is a Production System? Explain its components with a suitable example. Discuss the characteristics of production systems and their importance in AI problem solving.
    How to answer

    • Definition of Production System as rule-based problem solver
    • Components: global database, production rules, control strategy with example
    • Characteristics: modularity, commutativity etc.
    • Importance in AI search and problem solving
12. 7 Marks
    bExplain the concept of Knowledge Representation in detail. Discuss the characteristics of a good knowledge representation system and the challenges faced while representing real-world knowledge.
    How to answer

    • Concept and need of knowledge representation
    • Characteristics of good system: adequacy, efficiency, inferential etc.
    • Challenges: incompleteness, commonsense, real-world complexity
    • Brief overview of schemes: logic, semantic nets, frames
13. 7 Marks
    aExplain probabilistic reasoning in Artificial Intelligence. Discuss why probabilistic methods are required for reasoning under uncertainty. Explain its advantages and limitations.
    How to answer

    • Definition of probabilistic reasoning and uncertainty sources
    • Why deterministic logic insufficient; need for probabilities
    • Advantages: handles noise, graded belief; Limitations: data need, complexity
    • Applications in AI
14. 7 Marks
    bExplain Natural Language Processing (NLP). Describe the levels of language processing such as morphological, syntactic, semantic, and pragmatic analysis.
    How to answer

    • Definition and goal of NLP
    • Levels: morphological, syntactic, semantic, pragmatic, phonological
    • Purpose and techniques at each level with example
    • Pipeline view of language processing
15. 14 Marks
    Write short notes on any two of the following:

    1. a)Hill Climbing algorithm in detail.
    2. b)Benefits of expert system
    3. c)Explain the alpha-beta cut-off in game playing techniques
    4. d)Explain about A* algorithms in detail
    How to answer

    • Brief introduction to chosen two topics
    • Key principles, algorithm steps or benefits
    • Suitable example/diagram where applicable
    • Applications and conclusion