Artificial Intelligence

Core Course offered in the third year of Bachelor of Computer Engineering in Tribhuvan University.

Course Objectives:

The main objectives of this course are:

  1. To provide basic knowledge of Artificial Intelligence
  2. To familiarize students with different search techniques
  3. To acquaint students with the fields related to AI and the applications of AI


  1. Introduction (4 hrs)
    • Definition of Artificial Intelligence
    • Importance of Artificial Intelligence
    • AI and related fields
    • Brief history of Artificial Intelligence
    • Applications of Artificial Intelligence
    • Definition and importance of Knowledge, and learning.
  2. Problem solving (4 hrs)
    • Defining problems as a state space search
    • Problem formulation
    • Problem types, Well-defined problems, Constraint satisfaction problem,
    • Game playing, Production systems.
  3. Search techniques (5 hrs)
    • Uninformed search techniques - depth first search, breadth first search, depth limit search, and search strategy comparison
    • Informed search techniques -hill climbing, best first search, greedy search, A* search
    • Adversarial search techniques-minimax procedure, alpha beta procedure
  4. Knowledge representation, inference and reasoning (8 hrs)
    • Formal logic-connectives, truth tables, syntax, semantics, tautology, validity, well-formed-formula.
    • Propositional logic, predicate logic, FOPL, interpretation, quantification, horn clauses
    • Rules of inference, unification, resolution refutation system (RRS) , answer extraction from RRS, rule based deduction system
    • Statistical Reasoning-Probability and Bayes' theorem and causal networks, reasoning in belief network
  5. Structured knowledge representation (4 hrs)
    • Representations and Mappings
    • Approaches to Knowledge Representation
    • Issues in Knowledge Representation
    • Semantic nets, frames
    • Conceptual dependencies and scripts
  6. Machine learning (6 hrs)
  7. Applications of AI (14 hrs)


  1. E. Rich and Knight, Artificial Intelligence, McGraw Hill, 1991.
  2. D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall, 2001.
  3. P. H. Winston, Artificial Intelligence, Addison Wesley, 1984.
  4. Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
  5. Ivan Bratko, PROLOG Programming for Artificial Intelligence, Addison Wesley, 2001.
  6. Leon Sterling, Ehud Shapiro, The Art of PROLOG: Advanced Programming Techniques, Prentice Hall, 1996.

Laboratory Work:

Laboratory exercises should be conducted in either LISP or PROLOG. Laboratory exercises must cover the fundamental search techniques, simple question answering, inference and reasoning.