AL-304 Artificial Intelligence / Introduction to 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
   aWhat is production system? Explain it with an example. Discuss the characteristics of a production system.
   How to answer

   • Define production system: set of rules (condition-action), working memory, and control strategy / interpreter.
   • Provide an illustrative example (e.g., 8-puzzle or water jug rules).
   • Explain key components: database, production rules, rule applier/control strategy.
   • Discuss characteristics: modularity, explainability, uniform structure, independence of rules.
2. 7 Marks
   bGive an example of a problem for which breadth first search would work better than depth first search.
   How to answer

   • Identify scenarios where BFS outperforms DFS (e.g., shallow goal node in an infinite/deep search tree, finding shortest path in unweighted graph).
   • Give a concrete problem example (e.g., finding the shortest path on a social network/degrees of separation, or puzzle with shallow solution).
   • Explain why DFS fails or is inefficient (gets trapped in deep/infinite paths).
   • Contrast BFS guarantee of finding the shallowest/optimal path.
3. 7 Marks
   aDiscuss various approaches and issues in knowledge representation. Also discuss various problems in representing knowledge.
   How to answer

   • Define knowledge representation (KR) and its importance.
   • List approaches: Simple relational knowledge, Inheritable knowledge, Inferential knowledge, Procedural knowledge.
   • Discuss fundamental issues: Representational adequacy, Inferential adequacy, Inferential efficiency, Acquisitional efficiency.
   • Discuss problems: handling incomplete information, uncertainty, granularity, consistency.
4. 7 Marks
   bConsider the following statements and use predicate logic to express them formally : i) Marcus tried to assassinate Caesar. ii) All Pompeian's were Roman. iii) All Romans were either loyal to Caesar or hated him. iv) Everyone is loyal to someone. v) People only try to assassinate rulers they are not loyal to
   How to answer

   • Translate statement (i): $TryAssassinate(Marcus, Caesar)$
   • Translate statement (ii): $\forall x (Pompeian(x) \rightarrow Roman(x))$
   • Translate statement (iii): $\forall x (Roman(x) \rightarrow (LoyalTo(x, Caesar) \lor Hate(x, Caesar)))$ (or exclusive OR)
   • Translate statement (iv): $\forall x \exists y LoyalTo(x, y)$
   • Translate statement (v): $\forall x \forall y (Person(x) \land Ruler(y) \land TryAssassinate(x, y) \rightarrow \neg LoyalTo(x, y))$
5. 7 Marks
   aDesign a simple knowledge base using semantic networks for a weather prediction system.
   How to answer

   • Define semantic network structure (nodes = concepts/objects, directed edges = relationships like is-a, has-a, causes).
   • Design nodes: Weather, Season, Temperature, Humidity, Rain, Sunny, Clouds.
   • Establish links/relations: 'High Humidity' causes 'Precipitation', 'Dark Clouds' indicate 'Rain', 'Summer' has-attribute 'High Temperature'.
   • Provide a clear graphical diagram of the designed network.
6. 7 Marks
   bDefine Bayes's theorem. What are the applications of Bayes theorem?
   How to answer

   • State Bayes' theorem definition and state formula: $P(A|B) = \frac{P(B|A) P(A)}{P(B)}$.
   • Define all terms: prior probability $P(A)$, posterior probability $P(A|B)$, likelihood $P(B|A)$, evidence $P(B)$.
   • List key applications: Naive Bayes classification, medical diagnosis, spam filtering, robotics, recommendation systems.
7. 7 Marks
   aWhat is the Block World problem in AI planning? Explain with an example.
   How to answer

   • Define the Blocks World domain in automated planning.
   • Define standard predicates: $ON(A, B)$, $ONTABLE(A)$, $CLEAR(A)$, $HOLDING(A)$, $ARMEMPTY$.
   • Specify operators: Stack, Unstack, PickUp, PutDown with preconditions, add list, delete list.
   • Demonstrate with an example moving from an initial state to a goal state (e.g., reversing a stack of blocks).
8. 7 Marks
   bDevelop a simple AI-based chatbot using rule-based NLP.
   How to answer

   • Explain architecture of a rule-based chatbot (input preprocessing, pattern matching, response selection).
   • Describe NLP preprocessing steps: tokenization, lowercasing, stopword removal, stemming/lemmatization.
   • Define set of if-else / pattern rules or regular expressions for intents (e.g., greetings, FAQs, exit).
   • Provide pseudocode or Python snippet demonstrating rule-response mapping (e.g., ELIZA-style).
9. 7 Marks
   aDescribe the architecture and working of an expert system.
   How to answer

   • Draw block diagram of expert system architecture.
   • Explain main components: Knowledge Base, Inference Engine, Working Memory, User Interface, Explanation Facility, Knowledge Acquisition Module.
   • Explain working cycle: User query $\rightarrow$ Inference engine matches facts against rules in knowledge base $\rightarrow$ Solution/explanation generated for user.
10. 7 Marks
    bDesign a rule-based expert system for diagnosing diseases.
    How to answer

    • Specify the problem domain: Medical diagnosis of common illnesses (e.g., flu, malaria, allergy).
    • Define knowledge representation: symptom list (fever, cough, rash, fatigue).
    • Write IF-THEN production rules connecting symptoms to diseases.
    • Specify inference method: forward or backward chaining with conflict resolution.
    • Illustrate interaction with a sample case and diagnosis outcome.
11. 7 Marks
    aWhat is the motivation and need of AI in modern technology?
    How to answer

    • Describe motivation: automating repetitive tasks, handling vast amounts of data, mimicking human cognitive abilities.
    • Explain need in modern technology: scalability, accuracy, real-time decision making, solving complex problems.
    • Highlight domain applications: healthcare, finance, autonomous driving, manufacturing, NLP.
12. 7 Marks
    bHow a problem is formally defined? List down the components of it?
    How to answer

    • Define formal problem formulation in AI state space search.
    • List and define the 5 essential components: 1. Initial State, 2. Actions / Successor function, 3. Transition Model, 4. Goal Test, 5. Path Cost.
    • Briefly illustrate with an example such as the 8-puzzle or vacuum-cleaner world.
13. 7 Marks
    aShow how forward and backward reasoning are used in expert systems.
    How to answer

    • Define forward chaining (data-driven): starts from known facts and applies rules to deduce new facts until goal is reached.
    • Define backward chaining (goal-driven): starts from hypothesis/goal and works backward to find supporting facts.
    • Provide simple rule examples to demonstrate both directions.
    • Compare situations where each is preferred (e.g., diagnosis vs planning/monitoring).
14. 7 Marks
    bDescribe the Alpha-Beta pruning technique and its advantage.
    How to answer

    • Define Alpha-Beta pruning as an optimization technique for the minimax algorithm that eliminates branches that cannot influence final decision.
    • Explain parameters $\alpha$ (best value for MAX) and $\beta$ (best value for MIN).
    • State pruning condition: prune when $\alpha \ge \beta$.
    • State advantages: reduces branching factor, cuts search time from $O(b^d)$ to $O(b^{d/2})$ in ideal move ordering, allows deeper search.
15. 14 Marks
    Write short notes on (any two) a) Minimax algorithm b) Frames and Scripts c) Heuristic search algorithm d) Components of NLP
    How to answer

    • For (a) Minimax algorithm: explain game playing, MAX and MIN levels, recursive evaluation, utility values.
    • For (b) Frames and Scripts: define frames as slot-and-filler structures; scripts as structured representations for stereotypical event sequences with props, roles, tracks.
    • For (c) Heuristic search: define heuristic function $h(n)$, best-first search, A* algorithm with $f(n) = g(n) + h(n)$, admissibility.
    • For (d) Components of NLP: Natural Language Understanding (NLU) and Natural Language Generation (NLG); morphological, syntactic, semantic, pragmatic analysis.