Course

Title: Artificial Intelligence 

Instructor: Dr. Qaiser Abbas
Email: qaiser.abbas@uos.edu.pk
Web: www.clsp.org/qabbas

Course Code: CS-3811 , Credit Hours: 3, Prerequisites: Discrete Structures,
Office Hours: Friday: 3:00pm 4:00pm, or by appointment,
Department of Computer Science, University of Sargodha,
Sargodha, 40100, Pakistan

Course Objectives:
This course will introduce the basic principles in artificial intelligence. It will cover
simple representation schemes, problem solving paradigms, constraint propagation,
and search strategies. Areas of application such as knowledge representation,
natural language processing, expert systems, vision and robotics will be explored.
The Prolog programming language will also be introduced.

Course Syllabus:
What is AI, Foundations of AI, History of AI. Weak AI, Strong AI.Intelligent Agents:
Agents and Environments, The Nature of Environments, The Structure of Agents.
Problem Solving by Searching.Breadth-First Search, Depth-First Search, Depth-
limited Search, Iterative Deepening, Depth-first Search, Comparison of Uninformed
Search Strategies. Informed Search and Exploration.Constraint Satisfaction
Problems.Reasoning and Knowledge Representation.Inference in First-Order
Logic.Introduction to Prolog Programming.Reasoning Systems for
Categories.Reasoning with Uncertainty & Probabilistic Reasoning.Representing
Knowledge in an Uncertain Domain.Learning from Observations.Knowledge in
Learning. Statistical Learning, Neural Networks.
Course Outline:
1. Introduction: What is AI, Foundations of AI, History of AI. Intelligent Agents:
Agents and Environments, The Nature of Environments, The Structure of
Agents [TB: Ch. 1, 2]
2. Problem Solving by Searching: Problem Solving Agents, Searching for
Solutions, Uninformed Search Strategies.
3. Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative
Deepening, Depth-first Search, Comparison of Uninformed Search Strategies.
[TB: Ch. 3]
4. Informed Search and Exploration: Informed (Heuristic) Search Strategies:
Greedy Best- first Search, A* Search, Heuristic Functions, Local Search
Algorithms and Optimization Problems. [TB: Ch. 4]
5. Constraint Satisfaction Problems: Backtracking Search for CSPs, Local Search
for CSPs. Adversarial Search: Games, Minimax Algorithm, Alpha-Beta
Pruning. [TB: Ch. 5, 6]
 6. Reasoning and Knowledge Representation: Introductions to Reasoning and
Knowledge Representation, Propositional Logic, First Order Logic: Syntax
and Semantics of First- Order Logic, Knowledge Engineering in First-Order
Logic, [TB: Ch. 7, 8]
7. Inference in First-Order Logic: Inference rules for quantifiers, A first-order
inference rule, Unification, Forward Chaining, Backward Chaining, A
backward chaining algorithm, Logic programming, The resolution inference
rule [TB: Ch. 9]
8. Introduction to Prolog Programming
9. Reasoning Systems for Categories, Semantic Nets and Description logics,
Reasoning with Default Information: Open and closed worlds, Negation as
failure and stable model semantic. Truth Maintenance Systems [TB: Ch. 10]
10. Reasoning with Uncertainty & Probabilistic Reasoning : Acting Under
Uncertainty, Bayes' Rule and Its Use, [TB: Ch 13]
11. Representing Knowledge in an Uncertain Domain, The Semantics of Bayesian
Networks. [TB: Ch. 14]
12. Learning from Observations: Forms of Learning , Inductive Learning,,
Learning Decision Trees [TB: Ch. 18]
13. Knowledge in Learning, Explanation-Based Learning, Inductive Logic
Programming [TB: 19].
14. Statistical Learning, Neural Networks [TB: Ch. 20]

Textbook(s):
1. Artificial Intelligence: A Modern Approach, by Russell and Norvig, Prentice
Hall. 2ndEdition. ISBN-10: 0137903952
Reference Material:
1. Artificial Intelligence: A Systems Approach by M. Tim Jones, Jones and
Bartlett Publishers, Inc; 1stEdition (December 26, 2008). ISBN-10:
0763773379
2. Artificial Intelligence in the 21st Century by Stephen Lucci , Danny Kopec,
Mercury Learning and Information (May 18, 2012). ISBN-10: 1936420236
Term Paper:
For the term paper, students should follow IEEE or ACM transaction formats. For
example, your paper should have abstract, introduction, actual work, conclusion,
future work, and references.
NOTE:
For the term paper students are required to work in a group of two. Proposal for
Paper should include topic, idea (one paragraph), objective (why you want to study
the area one paragraph) and references. Papers without references, or material
used without quoting references may be treated as plagiarism with serious
consequences.
Lectures and Attendance Policy:
Most sessions will be the combination of lectures and discussions. Students are
expected to attend no less than 95% of the classes, be ready to begin the class on
time and not leave before the designated time. Students are also expected to come
prepared by going through the material to be discussed in each class beforehand
and participate in class discussions.
Evaluation:
Quiz(s)/Assignments 10
Mid Semester Evaluation 20
Final Semester Evaluation including term paper 45+15
Term Paper 10
Total: 100 points
Grading: A, B, C, and F

All deliverables are expected 100% on time. If the deliverable is not submitted on
due date, there will be a penalty of 20%. It will not be accepted once the deliverable
has been returned/discussed in class. Please discuss any issues in a timely manner
no consideration will be given at the end of the course.