MA S S E Y U N I V E R S I T Y
COLLEGE OF SCIENCES
Important Information about the paper
Paper Number and Title:
159302
Artificial Intelligence
Credits value:
15
Semester:
1
2
01
Campus:
Albany
Mode:
Internal
Calendar Prescription
:
AI programming. State
space representation and search. Heuristics. Planning. Game
playing. Knowledge representation. Knowledge

based systems. Natural language
processing. Machine learning. Reasoning under uncertainty. Philosophical issues.
Pre

requisites:
159.201 or 159.202 or
159.211
Co

requisites:
Restrictions:
159.318
E

Learning Category:
N/A
Paper Coordinator:
Dr
.
Napoleon
H.
Reyes
Office:
QA 2.56
Email:
N.H.Reyes@massey.ac.nz
Phone:
9512
Secondary Contact: N/A
Learning Outcomes:
On successful co
mpletion a student should be able to:
1.
Describe the main Artificial Intelligence techniques used in computing.
2.
Design and program computers to store and manipulate knowledge.
3.
Describe, and in some cases program, the special techniques that can be used
to pe
rform intelligent tasks such as searching, game playing, natural
language processing and machine learning by computers.
4.
Understand and use logic for inference and fuzzy logic and some of its
applications
5.
Identify the advantages and disadvantages of applyi
ng various AI techniques
in solving real world problems through exploring the various applications of
AI.
Additional Requirements for Paper Completion
To pass, students have to obtain a cumulative assessment score greater than or equal to 50%.
Final exam
ination dates:
http://www.massey.ac.nz/massey/study/exam/timetables/timetables_home.cfm
Timetable:
http://www.massey.ac.nz/massey/study/class

timetable/class

t
imetable_home.cfm
Monday 12:00

13:00 AT8
Thursday 12:00

13:00 AT8
Friday 12:00

13:00 AT5
Textbook and Other Recommended Reading, Online Resources:
Russell, S. and Norvig, P., Artificial Intelligence A Modern Approach, Third Edi
tion,
(Prentice Hall 2010), ISBN

13: 978

0

13

604259

4
Notes, assignment proposals, assignment submission, code examples at:
www.massey.ac.nz/~nhreyes
Percentage
Deadline of submission
Assignment 1
20%
of the available marks
April 5
Assignment 2
20% of the available marks
June
1
Final Exam
60% of the available marks
Lecture Outline and Teaching Schedule
Week 1. Introduction, Problem Solving Paradigm
•
Film viewing
•
Demonstration of AI applications
•
Tutorial: Simulation Essentials for the assignments
•
Introduction to Search
•
Background and Motivation
•
Examples of Graphs
•
Problem Solving Paradigm
•
Graph Search as Tree Search
•
Terminologies
•
Classes of Search
Week 2. Any

Path Search Examples
•
Depth

First Al
gorithm
•
Breadth

First Algorithm
•
Best

First Search Algorithm
•
Search Strategies
•
Issues of Implementing the Search Strategies
•
Analyses of Worst Case Scenarios
o
Cost and Performance
o
Any

Path Search (Uninformed and Informed, Using the Visited List)
o
Depth

First A
lgorithm
o
Breadth

First Algorithm
o
Best

First Search Algorithm
•
Tutorial: Problem Solving: Any

Path Search Algorithms
Week 3. Optimal Search: Part 1
•
Optimal Uninformed Search
•
Uniform Cost Search
•
Why not a Visited List?
•
Implementing Optimal Search Strategi
es
•
Optimal Informed Search
•
The A* Algorithm, Heuristics, Using the Strict Expanded List)
•
Tutorial: Problem Solving: Optimal Search Algorithms
Week 4. Optimal Search: Part 2
•
The A* and Expanded List
•
Uniform Cost and Strict Expanded List
•
Consistency
•
Optim
ality and Worst Case Complexity
•
Tutorial: Problem Solving: Optimal Search Algorithms
Week 5 & 6. Fuzzy Logic
•
Fuzzification & Defuzzification
•
Fuzzy logic operators
•
Fuzzy Inference Systems
•
Fuzzy Control Systems
•
Case Studies: Inverted Pendulum Problem, Ro
bot Navigation (Obstacle Avoidance
and Target Pursuit, Fuzzy Colour Processing
•
Tutorial: Problem Solving: Hand

simulation of the Complete Fuzzy Inference
System
Week 7 & 8. Machine Learning
•
Introduction to Neural Networks
•
Neural Network Architectures
•
B
ackpropagation Learning Algorithm
•
Case Study: Application to Pattern Recognition
•
Tutorial: Problem Solving: Backpropagation Learning Simulation by hand
Week 9. Constraint Satisfaction Problems and Games: Part 1
•
Binary CSP
•
Constraints
•
Constraint Propag
ation (Arc Consistency)
•
Constraint Propagation Example
•
Backtracking and Constraint Propagation
•
Backtracking with Forward Checking (BT

FC)
•
Tutorial: Problem Solving: Graph Colouring Problem
Week 10. Constraint Satisfaction Problems and Games: Part 2
•
BT

F
C with Dynamic Ordering
•
Incremental Repair
•
Introduction to Games
•
Board Games and Search
•
Alpha

Beta Pruning
•
Practical Matters
•
Tutorial: Problem Solving: Minimax, Alpha Beta Pruning.
Week 11. Thinking Logically
•
Syntax, Semantics, Proof System, Sentence
s
•
Logical Operators, Operator Truth Table
•
Inference Rules
•
Proofs
Week 12. Review for Finals
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