Principles of Artificial Intelligence
Fall 2012
Elective for ECE
Catalog Data: 

Description: 3 units. Provides an introduction to problems and techniques of artificial intelligence (AI). 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; implementing AI systems; advanced topics: artificial neural networks, and learning, genetic algorithms; survey of modern applications

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

May be convened with: ECE 479.

Usually offered: Fall.

ECE 373 or equivalent course

Artificial Intelligence: A modern approach. S. Russell and P. Norvig, Prentice Hall, 2009 (3rd Edition)

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.
Course Topics: 
  • 1. Introduction: What is Artificial Intelligence?
  • 2. Problem Solving
    2.1 Problems and Problem Spaces
    2.1.1 State space search
    2.1.2 Production systems
    2.1.3 Control Strategies
    2.1.4 Heuristic Search
    2.2 Basic Problem Solving Methods
    2.2.1 Forward and backward reasoning
    2.2.2 Problem trees and graphs
    2.2.3 The role of representation
    2.2.4 Search methods
    2.3 Game Strategies
    2.3.1. Minimax
    2.3.2 Alpha Beta Search
  • 3. Knowledge Representation (KR)
    3.1 Principles of KR using predicate logic
    3.2 Overview of KR using other logics
    3.3 Structured representations of knowledge
  • 4. Planning
    4.1 Blocks world problems
    4.2 Representation for planning
    4.3 Plan generating systems
  • 5. Survey of advanced topics and modern applications
Class/Laboratory Schedule: 

Two 90-minute lectures per week

Relationship to Student Outcomes: 

a) an ability to apply knowledge of mathematics, science, and engineering (Medium)
b) an ability to design and conduct experiments, as well as to analyze and interpret data (Medium)
c) 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)
d) an ability to function on multi-disciplinary terms (Low)
e) an ability to identify, formulate, and solve engineering problems (High)
f) an understanding of professional and ethical responsibility (Low)
g) an ability to communicate effectively (Medium)
h) the broad education necessary to understand the impact of engineering solutions in a global, economic,
    environmental, and societal context (Medium)
i)  a recognition of the need for, and an ability to engage in life-long learning (Medium)
j) a knowledge of contemporary issues (Medium)
k) 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