CS 331/CMPE 331 : Introduction to Artificial Intelligence
Instructor’s Name:
Office No. & Email:
M. M. Awais
402, [email protected]
Office Hours:
TBA
TA for the Course:
TBA
Year:
2010-2011
Quarter:
Spring
Category:
Junior
Course Code
(Units)
CS 331/CMPE 331 : Introduction to Artificial Intelligence
(3 Credit Hrs)
Course
Description
This course will introduce the basics of artificial intelligence (AI), its scope and
application domain. The course will cover topics such as knowledge
representation, propositional logic, predicate calculus, search methods, learning,
languages for AI programming, natural language representation, automated
reasoning, knowledge based systems and project implementation and knowledge
application.
Core/Elective
Elective
Pre-requisites
CS courses that include topics of general computing, data structures and
algorithms. Familiarity with at least one programming language and environment.
Goals
1. To introduce the principles of AI methods.
2. To equip students with the developments, justifications, implementation, and
use of representational, formalism and search methods.
3. To provide an opportunity to students to learn methods most useful under
complex computational uncertain, and vague situations.
Text/Referenc
e Books,
Programming
Environment,
etc.
A. Artificial Intelligence: Structures and Strategies for Complex Problem
Solving. (George F. Luger, and William A. Stubblefield).
B. Mathematical Methods in Artificial Intelligence. (Edward A. Bender).
C. Principals of Artificial Intelligence and Expert Systems Development.
(David W. Rolston)
D. Introduction to AI and Robotics (Robin R. Murphy)
Lectures
Two Sessions weekly
Grading
Assignments/Projects
Quizzes/Mini-Tests
Mid-Term Exam/Term Test
Final Exam
15%
20%
30%
35%
CS 331/CMPE 331: Introduction to Artificial Intelligence
Year:
Quarter:
Module
1
2
Topics




Introduction
History
Applications
Future
 Knowledge Representation with AI
Sessions
1
2,3,4
2010-2011
Spring
Readings
A) Chapter 1
A) Chapter 2
applications
 Propositional Logic
 Predicate Calculus
3
 Search Methods








4

A) Chapter 3, 4, 5
Introductions
State Space Search
 Depth First Search
 Breath first search
Heuristic search
Hill climbing
Best first search
A* method
Adversary search
 Alpha Beta Pruning
 Min Max Approach
Control and implementation of
search
 AI Languages

5,6,7,8
9,10,11,12
Standard vs AI languages
Prolog/LISP (Visual or otherwise)
Mid - Term Exam
13
A) Chapter 6, 7
CS 331/CMPE 331: Introduction to Artificial Intelligence
Year:
Quarter:
Module
5
Topics

Knowledge Database representation
 Introduction
 Expert System Design
 Architecture
 Case Study (MYCIN)
 Parallel Knowledge Data Discovery
 Handling Uncertainties
6

Natural Language Processing
 Introduction
 Syntax
 Semantics and Pragmatics
7
 AI and Robotics
 Learning Paradigms
 Agents (definition, design and
working)
Final Exam
Sessions
2010-2011
Spring
Readings
14,15,16,17
,19
A) Chapter 8
20,21
A) Chapter 10
22,23
Handouts(D)
24,25,26
A) Chapter 12
27,28
Handouts (D)