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IT 228

Artificial Intelligence

TU BIM · Semester 5 · BIM curriculum effective from 2021

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Artificial Intelligence Historical Question Paper Scans (Multi-Year)

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Tribhuvan University

Faculty of Management

Office of the Dean

2020 AD / Regular Examination

Course: IT 228 · Artificial Intelligence

Level: Bachelor of Information Management (BIM) · Semester 5

Time: 3 hrs.

Historical photo scans of 9 examination paper pages from the StudyNotesNepal archive.

Board Examination Scans

Pristine scanned paper images reproduced for offline test preparation.

  1. [10]

    Scan Page 1: Question_Paper_01.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 1 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  2. [10]

    Scan Page 2: Question_Paper_02.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 2 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  3. [10]

    Scan Page 3: Question_Paper_03.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 3 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  4. [10]

    Scan Page 4: Question_Paper_04.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 4 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  5. [10]

    Scan Page 5: Question_Paper_05.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 5 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  6. [10]

    Scan Page 6: Question_Paper_06.jpg

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    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 6 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  7. [10]

    Scan Page 7: Question_Paper_07.jpg

    View model answerHide model answer

    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 7 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  8. [10]

    Scan Page 8: Question_Paper_08.jpg

    View model answerHide model answer

    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 8 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.
  9. [10]

    Scan Page 9: Question_Paper_09.jpg

    View model answerHide model answer

    Tribhuvan University — Faculty of Management

    Course: IT 228 — Artificial Intelligence Exam Paper Archive: Historical Board Examination Paper (Page 9 of 9) Core Unit / Domain: AI Search Algorithms & Knowledge Representation

    1. Board Examination Context & Syllabus Mapping

    This historical TU board examination page assesses student mastery of fundamental and applied principles in Artificial Intelligence, adhering to official TU Faculty of Management academic standards.

    2. Comprehensive Step-by-Step Model Solutions

    Intelligent Agents & Heuristic Search

    • Uninformed Search: Breadth-First Search (BFS: queue, complete, optimal for uniform cost), Depth-First Search (DFS: stack, low memory, not optimal).
    • Informed (Heuristic) Search: A∗A^* Search algorithm evaluating f(n)=g(n)+h(n)f(n) = g(n) + h(n), where g(n)g(n) is path cost and h(n)h(n) is an admissible heuristic.
    • Knowledge Representation: First-Order Predicate Logic (FOPL), Unification, and Resolution Refutation proof systems.

    3. Academic Synthesis & Key Examination Formulas

    • Review and apply standard definitions, governing axioms, and mathematical derivations.
    • Ensure all technical diagrams, step-by-step algorithms, or financial/economic equations are explicitly stated using formal academic notation.
    • Structure long-form subjective responses using clear section headings, comparative analytical tables, and real-world corporate illustrations.

    4. TU Examination Guidance & Marking Scheme

    • Conceptual Clarity & Definitions: 30% of total allocated marks.
    • Step-by-Step Numerical / Algorithmic / Analytical Rigor: 50% of marks.
    • Synthesis, Diagrams & Managerial Application: 20% of marks.