King Fahd University of Petroleum & Minerals

College of Computer Sciences and Engineering

Information and Computer Science Department

ICS 381: Principles of Artificial Intelligence (3-0-3) [Core Course]

Syllabus – Fall Semester 2012-2013 (121) [PDF]

Website:

All course material and resources are posted in Blackboard (WebCT) http://webcourses.kfupm.edu.sa

Class Time, Venue and Instructor Information:

Course Catalog Description

Introduction to Artificial Intelligence (AI), history and applications; First order logic; State space representation; Blind and heuristic search; Constraint satisfaction and planning; Knowledge representation; Reasoning in uncertain situations; Machine learning; Prolog programming; Natural language processing; Expert systems and real AI applications.

Pre-requisites:  ICS 253: Discrete Structures I (or Equivalent)

Course Objectives

Provide students with in-depth knowledge of important concepts, problems solving, and techniques in AI

Introduce students to the basic toolkit of AI algorithms and representation methods that can be applied to a wide variety of real world problems.

Course Learning Outcomes

Upon completion of the course, you should be able to:

1. Differentiate and analyze the concepts of optimal reasoning and optimal behavior compared to human-like reasoning and behavior.
2. Design, implement, and evaluate uninformed, heuristic, and adversarial search algorithms, such as depth-first, A*, Minimax, and alpha-beta pruning.
3. Apply knowledge-based reasoning through modeling of resolution, propositional and predicate logic, and probabilistic modeling.
4. Transform AI algorithms (e.g., A*, IDA*, Genetic Algorithm) into any programming language (e.g., Python, C#, Java, etc.)
5. Apply basic AI algorithms, techniques and representation methods to a wide variety of real world problems.

Required Material

     Artificial Intelligence: A Modern Approach, 3/E. By Stuart Russell & Peter Norvig, Prentice Hall, 2010. http://aima.cs.berkeley.edu/

    Lecture Handouts   (via Blackboard)

Other Recommended References

         Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6/E. By George F. Luger, Addison-Wesley Publisher, 2009.

          Artificial Intelligence: A Guide to Intelligent Systems, 2/E. By  Michael Negnevitsky, Addison-Wesley Publisher, 2005.

          AI Algorithms, Data Structures and Idioms in Prolog, Lisp and Java. By G.F. Luger & W.A. Stubblefield, Pearson Education, 2009.    (PDF)

          Prolog Programming for Artificial Intelligence, 4/E. Ivan Bratko, Addison-Wesley Publisher, 2012.

          An Introduction to Prolog Programming, Lecture Notes, King's College London and University of Amsterdam, 1999-2007. (PDF)

Assessment Plan

Tentative Schedule

How to do well and become a star?

Sample AI Applications

Related AI Books

Other Resources on the Web:

[Sorted Collection is posted in Blackboard but here is a sample]

• McCarthy: What is AI?

• The AI Dictionary

• Humans Versus Computers

• Making Robots Conscious of their Mental States

• AAAI

• JAIR

• MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL)

• Top Authors in AI 
• McCarthy: Lecture Notes
• Artificial Intelligence Books
• Top authors in Artificial Intelligence
• Artificial Intelligence at Georgia Tech
• Stanford University CS 221 Introduction to Artificial Intelligence
• Intro to AI - Course overview
• Artificial Intelligence (AI) - EECS at UC Berkeley
• AI Course at New York Univ.
• Pattern Analysis and Machine Intelligence (PAMI) research group
• Centre for Computational Intelligence
• European Centre for Soft Computing
• DATA MINING AND MACHINE LEARNING GROUP AT UNIVERSITY OF HOUSTON
• Intelligent Systems Research Group at KFUPM