Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

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Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

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Previous Year Questions

Lesson 34 of 34 in the free Artificial Intelligence notes on Siksha Sarovar, written by Rohit Jangra.

Artificial Intelligence — Previous Year Questions

PYQ papers for this course will be added here soon.

Check back later for:

  • End Term Examination papers
  • Mid Term papers
  • Important questions by unit

Frequently Asked Topics (by unit)

Unit 1

  • Define AI. Explain the four approaches to defining AI (thinking/acting x humanly/rationally)
  • Explain the importance and applications of AI with suitable examples
  • Explain AI and its related fields (any four)
  • What are AI techniques? Explain the characteristics of a good AI technique
  • Explain the Turing Test with a diagram; discuss its criticisms
  • Formulate the water jug problem (or 8-puzzle) as a state space search problem
  • Explain production systems and their types (monotonic, non-monotonic, commutative)
  • Discuss the issues in the design of a search problem
  • Explain the generate-and-test technique with an example
  • Explain hill climbing; discuss local maximum, plateau and ridge problems with solutions
  • Explain the best-first search algorithm and the A* algorithm; explain admissibility of a heuristic
  • Explain the AO* algorithm / problem reduction with an AND-OR graph example
  • Formulate the map-colouring or cryptarithmetic problem as a CSP; explain backtracking

Unit 2

  • Define knowledge; explain its importance in AI systems
  • Explain the approaches used in knowledge representation (semantic nets, frames, scripts, production rules)
  • Discuss the issues in knowledge representation
  • Represent the given English sentences in predicate logic (facts, quantifiers)
  • Explain how INSTANCE and ISA relationships support inheritance, with an example
  • Explain computable functions and predicates; differentiate decidable and undecidable predicates

Unit 3

  • Differentiate syntactic, semantic, discourse and pragmatic processing with examples
  • Explain structural ambiguity with an example sentence and its two parse trees
  • What is learning? Explain the general learning model (performance element, critic, learning element)
  • Explain rote learning with a suitable example
  • Explain learning by taking advice; why is operationalising advice difficult?
  • Explain learning in problem solving using macro-operators
  • Explain inductive learning / learning from examples with the version space (S and G boundaries)
  • Explain explanation-based learning; differentiate it from inductive learning

Unit 4

  • Draw and explain the architecture of an expert system
  • Differentiate forward chaining and backward chaining with examples
  • Write a sample production rule for a given domain (medical/financial/agricultural)
  • Explain the knowledge acquisition bottleneck
  • What is an expert system shell? Explain with the MYCIN to EMYCIN example
  • Explain the features of LISP that make it suitable for AI programming
  • Differentiate LISP and PROLOG