Artificial Intelligence

randombroadAI and Robotics

Oct 15, 2013 (3 years and 10 months ago)

100 views

Artificial Intelligence

Course Name:
Artificial Intelligence.


Course Code:
IT
F
308

Credit hours:
3

Knowledge Domain:
IT foundations.

Prerequisite(s):

Discrete Mathematics (GEN
206)
-

Automata Models




(CAS
205)


Learning Objectives

Upon completion
of this course, the student will be able to:

1.

Present the scope of AI and applications in problem solving.

2.

Grasp the

Knowledge representation and expert systems.

3.

Acquire

planning techniques& machine learning.


Learning Outcomes

1.

Grasping the basic AI problem

solving techniques.

2.

Grasping knowledge representation aspects and the basic
components of expert systems and their applications.

3.

Acquaintance with the different types of Machine learning, and
basic planning& game playing concepts.


Overview and Syllabus

O
utline of Artificial Intelligence. Problem solving techniques.
Knowledge representation issues. Representing knowledge using
predicate logic and rules. Game playing. Planning. Machine learning.
Expert systems.


Course Outline



Topic

Lecture
Hours

1

Outli
ne of Artificial Intelligence

The scope of AI. Turing test. Overview of AI techniques.

3

2

Problems Solving Techniques

Problems as state space search. Production systems and
their characteristics. Heuristic search techniques.

6

3

Knowledge representation

issues

Approaches to knowledge representation. Issues in
knowledge representation. The frame problem.

3

4

Representing knowledge using predicate logic and
rules

Representing simple facts, instances and relationships.
Resolution. Natural deduction. Decla
rative knowledge.
Logic programming. Forward& background reasoning.
6

Control knowledge.

5

Game Planning

The minimax search procedure. Alpha
-
Beta cutoffs.
Specific example.

6

6

Planning

Components of a planning system. Goal stack planning.
Nonlinear pla
nning. Hierarchical planning.

6

7

Machine Learning

Different types of learning. Learning from examples.
Explanation
-
based learning. Discovery. Analogy.

6

8

Expert Systems

Representing and using domain knowledge. Expert system
shells. Explanation. Knowled
ge acquisition.

6