159.302
Computer Science
Institute of Information and Mathematical Sciences email: n.h.reyes@massey.ac.nz
Tel. No.: 64 9 4140800 x 9512 / 41572
Fax No.: 64 9 441 8181
Lectures:
Monday 12pm – 1pm AT8
Thursday 12pm – 1pm AT8
Friday 12pm – 1pm AT5
Office hours: after lectures (QA2.56 or
IIMS Lab 7) http://www.massey.ac.nz/~nhreyes/Massey/159302.html

Pre-requisites
Course Overview
Learning Outcomes
Assessment
Course Schedule
AI Demonstrations

Note:
If a student cannot attend lectures/tutorials it is the student’s responsibility to find out what
(possible changes to assignments, questions
& answers).

*
To take this course you must have passed 159.201
since
C or C++ knowledge is required to complete the assignments

Teaching Approach
Discussion of the Theoretical Framework
Step-by-Step Algorithm Details
Application to real-world problems
Simulations (A Graphics Engine will be provided to make learning AI more fun)

On successful completion of the course, the students should be able to:
Understand the concepts and theories behind AI technologies
Implement selected AI algorithms

Main text book
Russell S. and Norvig P., Artificial Intelligence A Modern
Approach, 3rd Ed, Prentice Hall 2009
ISBN-13: 9780136042594
MIT OpenCourseWare
Neural Network and Fuzzy Logic Applications in C/C++ by Stephen
T. Welstead
Artificial Intelligence: Structures and Strategies for Complex
Problem Solving by George F Luger

2 assignments: 40%
Final Exam (3 hours): 60%
To pass, students have to obtain a cumulative assessment score greater than or equal to 50%.
Deadline: Deadlines for assignments will be given when assignments are distributed. You will be given 4 weeks to complete each assignment
Penalty: Late submissions (up to 1 week) will be penalised by 10%.

Program solutions that do not compile or do not run in our laboratories get 0 marks.
Late assignments will be penalized all members of the group should be named in the source file of each assignment, including the contribution of each member.
All submitted assignments will have to be accompanied by a short documentation as well.
There can be at most 3 members in a group.

Each group member will receive the same grade.
Students in a team have the authority (in consultation with the lecturer) to "expel" any member that does not meet obligations .
The collaboration is limited only to members within each group.
It is a student responsibility to check their assignment marks and notify in writing any errors they might find no later than 10 days after the day the marks were made available.

• I’ll be collecting emails for making announcements to the class from time to time
• Please send me an email with the following
Subject heading : 159302
• In your email, please write your name, ID
number, degree you’re pursuing and your
technical background (e.g. C++ programming)

Week 1.
• Introduction, Intelligent Agents.
• Tutorial: Simulation Essentials for the assignments
Week 2. Introduction to Search
• Background and Motivation
•
•
• Examples of Graphs
Graph Search as Tree Search
• Terminologies
• Classes of Search
Week 3. Search Strategies
• Issues of Implementing the Search Strategies
• Cost and Performance
• Any-Path Search (Uninformed and Informed, Using the Visited List)
• Depth-First Algorithm
• Breadth-First Algorithm
• Best-First Search Algorithm

Week 4. Any-Path Search Examples
• Depth-First Algorithm
• Breadth-First Algorithm
• Best-First Search Algorithm
• Tutorial: Problem Solving: Any-Path Search Algorithms
•
•
Week 5. Optimal Search: Part 1
Uniform Cost Search
• Why not a Visited List?
• Implementing Optimal Search Strategies
• Optimal Informed Search
• The A* Algorithm, Heuristics, Using the Strict Expanded List)
• Tutorial: Problem Solving: Optimal Search Algorithms
Week 6. Optimal Search: Part 2
• The A* and Expanded List
• Uniform Cost and Strict Expanded List
• Consistency
• Optimality and Worst Case Complexity
• Tutorial: Problem Solving: Optimal Search Algorithms

Week 7. Fuzzy Logic

Fuzzification, Defuzzification, Fuzzy logic operators, Fuzzy Inference Systems,
Fuzzy Control Systems
 Inverted Pendulum Problem, Robot Navigation, Colour Object Recognition
Week 8. Machine Learning


Pattern Recognition
Week 9. Constraint Satisfaction Problems and Games: Part 1

Binary CSP

Constraints

Constraint Propagation (Arc Consistency)
 Constraint Propagation Example

Backtracking and Constraint Propagation

Backtracking with Forward Checking (BT-FC)

Week 10. Constraint Satisfaction Problems and Games: Part 2
• BT-FC with Dynamic Ordering
• Incremental Repair
• Introduction to Games
• Board Games and Search
• Alpha-Beta Pruning
•
• Practical Matters
*Week 11. Propositional Logic & First Order Logic
• Syntax, Semantics, Proof System, Sentences
• Semantic Rules
• Satisfiability
• Satisfiability Problems
• Propositional Logic Proof
• Natural Deduction, Proof Systems, Conjunctive Normal Form, Propositional Resolution
• Natural Language Processing
Week 11. Alternatively, Genetic Algorithms + Propositional Logic could be taught.
Week 12. Review for Finals

Search Algorithms (Tree Search + Heuristics)
Sample application: 8-Puzzle
Fuzzy Logic – based on how we humans think
Sample applications: Robot Navigation, Inverted Pendulum
Neural Network – based on the architecture of the brain
Sample application: pattern recognition
Genetic Algorithm – based on theory of evolution
Sample application: optimisation

C:\Core\Massey Papers\159302\Assignments 2008\Assign #1 - 2008\8 Puzzle - beta v.3.0

Otherwise known as Broom-Balancing Problem
The mathematical solution uses a secondorder differential equation that describes cart motion as a function of pole position and velocity:

 m
 2
 t
2
( x
 l sin


)
 l cos
 

 m
 2
 t
2
( l cos


)
 l sin
  mgl sin

Input: x, v, theta, angular velocity Output: Force, direction

Fuzzy rule base and the corresponding FAMM for the velocity and position vectors of the inverted pendulum-balancing problem
1. IF cart is on the left AND cart is going left THEN largely push cart to the right
2. IF cart is on the left AND cart is not moving THEN slightly push cart to the right
3.
IF cart is on the left AND cart is going right THEN don’t push cart
4. IF cart is centered AND cart is going left THEN slightly push cart to the right
5.
IF cart is centered AND cart is not moving THEN don’t push cart
6. IF cart is centered AND cart is going right THEN slightly push cart to the left
7.
IF cart is on the right AND cart is going left THEN don’t push cart
8. IF cart is on the right AND cart is not moving THEN push cart to the left
9. IF cart is on the right AND cart is going right THEN largely push cart to the left

Inverted Pendulum Problem
If the cart is too near the end of the path, then regardless of the state of the broom angle push the cart towards the other end.
If the broom angle is too big or changing too quickly, then regardless of the location of the cart on the cart path, push the cart towards the direction it is leaning to.
X
N ZE P
N PL ZE ZE
X’ ZE ZE ZE ZE
P ZE ZE NL

N ZE P
N NL NM ZE
 ’ ZE NM ZE PM
P ZE PM PL
Input: x, v, theta, angular velocity Output: Force, direction

Obstacle Avoidance, Target Pursuit, Opponent Evasion
Input:
Multiple Obstacles: x, y, angle
Target’s x, y, angle
Output: Robot angle, speed

Multiple Fuzzy Systems employ the various robot behaviours
Path planning Layer:
The A* Algorithm
Next Waypoint
Target Pursuit
Adjusted Angle
Fuzzy System 2:
Central
Control
Adjusted Speed
ObstacleDistance <
MaxDistanceTolerance and closer than Target
N
Fuzzy System 4:
Y
Obstacle Avoidance
Adjusted Angle
Avoidance
Adjusted Speed
Actuators
Target
Pursuit
Obstacle
Avoidance

Input: Obstacles’ x, y, angle
Target’s x, y, angle
Output: Robot angle, speed
C:\Core\Massey Papers\159302\Assignments 2008\Assign #2 - 2008\Robot Navigation - v.9.4 - FL-AStar

3-D Hybrid Fuzzy A* Navigation System
Cascade of Fuzzy Systems

• Beauty-of-nature argument
• How Life Learned to Live
(Tributsch, 1982, MIT
Press)
• Example: Nature as structural engineer

f ( x )
 n =10 c = 2 30 -1 = 1,073,741,823 x c n
26

• Function to evaluate: x coeff
 10

Fitness Function or Objective
Function
• coeff – chosen to normalize the x parameter when a bit string of length lchrom =30 is chosen.
coeff

2
30 
1
• Since the x value has been normalized, the max. value of the function will be:
( )

1.0
x

2
30

1
27

• With a string length=30, the search space is much larger, and random walk or enumeration should not be so profitable.
• There are 2 30 =1.07(10 10 ) points.
With over 1.07 billion points in the space, oneat-a-time methods are unlikely to do very much very quickly.
28
Also, only 1.05 percent of the points have a value greater than 0.9.

29
Initial population of chromosomes
Offspring
Evolutionary operations
Population
No
Calculate fitness value
Solution
Found?
Yes
Stop

with

IIMS Lab 7
Overhead
Camera
Colour objects
Fluorescent lamps www.Fira.net
*
Exploratory environment is indoor – room totally obstructed from sunlight
Multiple monochromatic light sources – fluorescent / fluoride lamps
Colour Object Recognition (Recognition speed: < 33ms)

*
HARDWARE OUTLINE
Camera Frame Grabber
3D Scene
2D Digital
Image
Image Sensors
CID (Charge Injection Device)
CCD (Charge Coupled Device)
PDA (Photo Diode Array)
Emmitted light

Colour constancy is inherent in us humans, but not in cameras.
Yellow object turns pale under strong white illumination
Color is not captured by the camera as we humans see it.
A Green object tends to appear more as a whitish yellow object under bright white illumination.

Colour objects traversing the field under spatially varying illumination intensities
Other Factors:
Lens focus
Object rotation
Quantum electrical effects
Shadows
Presence of similar colours
Bright
Dark
Dim
*
We need to automatically compensate for the effects of varying illumination intensities in the scene of traversal

Experiments performed at IIMS Lab 7
To some extent, the algorithm can see in the dark
Applying the colour contrast operations to compensate for the effects of glare, hue and saturation drifting also allows for colour correction

Experiments performed at IIMS Lab 7
*
PINK colour patches can be amplified to revert back close to its original colour

The Fuzzy Vision algorithm employed in the game…
Robots in Massey (QA 2.42)
C:\Core\Research\Conferences\ICONIP08
Old system