Principles of Artificial Intelligence
Fall 2015
Catalog Data: 

ECE 479 -- Principles of Artificial Intelligence (3 units)

Description: The course provides an introduction to problems and techniques of artificial intelligence (AI). Topics covered inlcude automated problem solving, methods and techniques; search and game strategies; knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; and implementing AI systems.

Grading: Regular grades are awarded for this course: A B C D E

May be convened with ECE 579

ECE 373

Russell, Stuart and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd Ed. Pearson. 2009.

Course Learning Outcomes: 

By the end of this course, the student will be able to:

  1. Demonstrate the ability to solve combinatorially complex problems by using heuristic techniques.
  2. Construct knowledge representations and apply them as the foundation for design and analysis of complex, computer-based systems.
  3. Demonstrate an understanding of planning techniques, construct plans and plan generating systems.
  4. Design knowledge-based systems.
  5. Design and implement reasoning engines and theroem provers.
Course Topics: 

What is Artificial Intelligence?

Problems and problem spaces

  • State space search
  • Production systems
  • Control strategies
  • Heuristic Search

Basic problem solving methods

  • Forward and backward reasoning
  • Problem trees and graphs
  • The role of representation
  • Search methods

Game Strategies

  • Minimax
  • Alpha Beta Search

Knowledge Representation (KR)

  • Principles of KR using predicate logic
  • Overview of KR using other logics
  • Structured representations of knowledge


  • Blocks world problems
  • Representation for planning
  • Plan generating systems

Advance topics including, but not limited to:

  • Computer-guided surgery
  • Intelligent sensing systems
  • Co-evolution
  • Game theory
  • Big data science
Class/Laboratory Schedule: 

Two, 75-minute lectures per week

Relationship to Student Outcomes: 

ECE 479 contributes directly to the following specific Electrical and Computer Engineering Student Outcomes of the ECE department:

  • an ability to apply knowledge of mathematics, science and engineering (Medium)
  • an ability to design and conduct experiments, as well as to analyze and interpret data (Medium)
  • an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (Medium)
  • an ability to function on multi-disciplinary teams (Low)
  • an ability to identify, formulate and solve engineering problems (High)
  • an understanding of professional and ethical responsibility (Low)
  • an ability to communicate effectively (Medium)
  • the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental and societal context (Medium)
  • a recognition of the need for, and an ability to engage in life-long learning (Medium)
  • a knowledge of contemporary issues (Medium)
  • an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice (High)
Prepared by: 
Dr. Jerzy Rozenblit
Prepared Date: 

University of Arizona College of Engineering