University of Information Technology

Artificial Intelligence

Course Description

This course introduces the foundations of artificial intelligence. The ultimate aim of artificial intelligence (A.I.) is to understand intelligence and to build intelligent software and robots that come close to the performance of humans. On their way towards this goal, A.I. researchers have developed a number of quite different subdisciplines. This course presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning.

Topics include in this course are Introduction to AI, Propositional Logic, First-order Predicate Logic, Limitations of Logic, Logic Programming with PROLOG, Search, Games and Problem Solving, Reasoning with Uncertainty and Machine Learning and Data Mining.

The aims of this course are:

  • Introduce principles and techniques of Artificial Intelligence (AI).
  • Explain how Artificial Intelligence enables capabilities that are beyond conventional technology, for example, chess-playing computers, self-driving cars, robotic vacuum cleaners.
  • Describe the different types of intelligent agents.
  • Understand the Logical problem solving with logic programming.
  • Analyze the different types of search algorithms and role of heuristics.
  • Clarify the basic knowledge representation, reasoning in uncertain problems and Learning methods of AI.

Intended Learning Outcomes

Upon the successful completion of this course, students should be able to:

  • Develop the intelligent systems by assembling solutions to concrete computational problems.
  • Implement problem solving schemes including representation and reasoning in terms of logic programming.
  • Implement state-space search algorithms for a variety of problems.
  • Solve problems with noise and uncertainty using probabilistic techniques.
  • Understand basic concepts of machine learning & data mining techniques.

Text and References Books


  1. Introduction to Artificial Intelligence (Undergraduate Topics in Computer Science) by Wolfgang Ertel, Nathanael T. Black, Springer; 2011 edition (March 15, 2011)
  2. Artificial Intelligence A Modern Approach Third Edition by Stuart J. Russell and Peter Norvig


  1. Computational Intelligence, A Methodological Introduction Series: Textbook in Computer Science by Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P. Springer; 2013 edition.
  2. SWI Prolog Reference Manual 6.6.6 Edition May, 2016
  3. Prolog Programming for Artificial Intelligence by Ivan Bratko, Pearson Canada; Fourth Edition, 2011.

Assessment system

Evaluation Marks Percentage
Class Participation 10 Marks 10%
Tutorial/Assignment 10 Marks 10%
Project 10 Marks 10%
Discussion/Presentation 10 Marks 10%
Final Examination 60 Marks 60%