ICS 381: Principles Of Artificial Intelligence

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ICS 381: Principles Of Artificial Intelligence

 
Course Information
Class/Laboratory Schedule: 

3 * 50-minute lectures per week. No lab. (3-0-3)

Designation: 
 Required Course
Course Level: 
 Undergraduate
Prerequisites
Prerequisite(s) by Topic: 
  • Basic concepts in discrete structures, probability, calculus, and linear algebra.
  • Programming Fundamentals.
Prerequisite Courses: 
ICS 253: Discrete Structures I
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, Advanced AI applications.

Textbook(s): 

Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, (Second Edition) 2003.

Reference(s) and Other Material: 
  • Artificial Intelligence: Structures and Strategies for Complex Problem Solving, George F. Luger, Addison Wesley Publisher, (Fifth Edition) 2005.
  • Prolog Programming for Artificial Intelligence, Ivan Bratko, Addison Wesley, (Third Edition) 2001.
Course Outcomes: 

After completion of this course, the student shall be able to:

  • Understand the meaning of AI, its alternative approaches and the implications of AI for cognitive science more broadly.
  • Expand their knowledge about mechanisms, semantic networks, frame systems, heuristic search, genetic algorithm, planning, and symbolic learning algorithms.
  • Understand the basic methods in planning and reasoning using both logic and uncertain inference.
  • Know a variety of ways to represent and retrieve knowledge and information [Expert
    systems, Agents].
  • Know the fundamentals of AI programming techniques and advanced machine learning in a modern programming language.
Topics Covered: 
  • AI history and applications.
  • Intelligent Agents.
  • Problem Solving by Searching.
  • Constraint Satisfaction Problems.
  • Informed Search and Exploration.
  • Adversarial Search
  • First Order Logic.
  • Inference in First Order Logic.
  • Knowledge Representation and Knowledge-Base System.
  • Planning Systems.
  • Reasoning in Uncertain Situations.
  • Machine Learning.
  • Languages and Programming Techniques for AI (Prolog, Lisp).
  • Natural Language Processing.
  • Communicating, Perceiving, and Acting.
  • Advanced Applications of AI.​