1. Artificial Intelligence
Dr. Napoleon H. Reyes, Ph.D.
Computer Science
Institute of Information and Mathematical Sciences
Artificial Intelligence
Rm QA, IIMS, Albany Campus or IIMS Lab 7
Tel. No.: x 9512 / 41572
Fax No.:
Lectures:
Monday pm – 1pm AT8
Thursday pm – 1pm AT8
Friday pm – 1pm AT5
Office hours: after lectures (QA2.56 or IIMS Lab 7)

2. Topics for Discussion Pre-requisites Course Overview Learning Outcomes
Texts and Course Material
Assessment
Course Schedule
AI Demonstrations

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

4. *
To take this course you must have passed since C or C++ knowledge is required to complete the assignments
Pre-requisites

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

6. On successful completion of the course, the students should be able to:
Understand the concepts and theories behind AI technologies
Learning Outcomes
Learn how to apply AI techniques to different problems
Implement selected AI algorithms

7. Texts and Course Material
Main text book
Russell S. and Norvig P., Artificial Intelligence A Modern Approach, 3rd Ed, Prentice Hall 2009
ISBN-13:  
Texts and Course Material
Other References
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

8. Assessment 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%.

9. Program solutions that do not compile or do not run in our laboratories get 0 marks.
Late assignments will be penalized
Assessment
Assignments may be completed in groups
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.

10. Assessment 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 .
Assessment
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.

11. Week 1.
Introduction (chap 1), Philosophical Issues (chap 26 & 27), Intelligent Agents (chap 2).
Film viewing
Tutorial: Simulation Essentials for the assignments
Week 2. Introduction to Search
Background and Motivation
Examples of Graphs
Problem Solving Paradigm
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
Course Schedule

12. Course Schedule 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
Optimal Uninformed Search
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
Course Schedule

13. Course Schedule 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
Neural Networks
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)
Course Schedule

14. 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
Tutorial: Problem Solving: Minimax, Alpha Beta Pruning.
*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
Course Schedule

15. Demonstrations 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

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

17. Control System: Inverted Pendulum Problem
Otherwise known as Broom-Balancing Problem
The mathematical solution uses a second-order differential equation that describes cart motion as a function of pole position and velocity:
Input: x, v, theta, angular velocity
Output: Force, direction

18. Fuzzy Rules
Fuzzy rule base and the corresponding FAMM for the velocity and position vectors of the inverted pendulum-balancing problem
IF cart is on the left AND cart is going left THEN largely push cart to the right
IF cart is on the left AND cart is not moving THEN slightly push cart to the right
IF cart is on the left AND cart is going right THEN don’t push cart
IF cart is centered AND cart is going left THEN slightly push cart to the right
IF cart is centered AND cart is not moving THEN don’t push cart
IF cart is centered AND cart is going right THEN slightly push cart to the left
IF cart is on the right AND cart is going left THEN don’t push cart
IF cart is on the right AND cart is not moving THEN push cart to the left
IF cart is on the right AND cart is going right THEN largely push cart to the left

19. Fuzzy Control System 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 PL X’ NL  N ZE P NL NM ’ PM PL
Input: x, v, theta, angular velocity
Output: Force, direction

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

21. Cascade of Fuzzy Systems
Multiple Fuzzy Systems employ the various robot behaviours
Path planning Layer:
The A* Algorithm
Path Planning Layer
Next Waypoint
Fuzzy System 1
Fuzzy System 1: Target Pursuit
Target
Pursuit
Adjusted Angle
Central
Control
Fuzzy System 2: Speed Control for Target Pursuit
Fuzzy System 2
Adjusted Speed
ObstacleDistance < MaxDistanceTolerance and closer than Target N
Here’s the general outline of our colour contrast algorithm Y
Fuzzy System 3: Obstacle Avoidance
Fuzzy System 3
Obstacle
Avoidance
Adjusted Angle
Fuzzy System 4: Speed Control for Obstacle Avoidance
Fuzzy System 4
Adjusted Speed
Actuators

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

23. 3-D Hybrid Fuzzy A* Navigation System Cascade of Fuzzy Systems
Simulations
Hybrid Fuzzy A*
3-D Hybrid Fuzzy A* Navigation System
Cascade of Fuzzy Systems

24. Nature as Problem Solver
Beauty-of-nature argument
How Life Learned to Live (Tributsch, 1982, MIT Press)
Example: Nature as structural engineer
Increase moment of inertia to resist bending
Helical stiffner – insect windpipe
Physics. The tendency of a body to resist acceleration; the tendency of a body at rest to remain at rest or of a body in straight line motion to stay in motion in a straight line unless acted on by an outside force.

25. Genetic Algorithm
Let’s see the demonstration for a GA that maximizes the function
n =10
c = = 1,073,741,823

26. Fitness Function or Objective Function
Simple GA Example
Function to evaluate:
coeff – chosen to normalize the x parameter when a bit string of length lchrom =30 is chosen.
Since the x value has been normalized, the max. value of the function will be:
when for the case when lchrom=30
Fitness Function or Objective Function

27. Test Problem Characteristics
With a string length=30, the search space is much larger, and random walk or enumeration should not be so profitable.
There are 230=1.07(1010) points.
With over 1.07 billion points in the space, one-at-a-time methods are unlikely to do very much very quickly.
Also, only 1.05 percent of the points have a value greater than 0.9.

28. Simple GA Implementation
Initial population of chromosomes
Offspring
Population
Calculate fitness value
Solution
Found?
Evolutionary
operations No
Yes
Stop

29. Identifying Colour Objects
FIRA RoboWorld Congress & CIRAS 2005
Identifying Colour Objects
with
Fuzzy Colour Contrast Fusion

30. Robot Soccer Set-up * IIMS Lab 7
Overhead Camera
Fluorescent lamps
Colour objects
Next, let’s have a look at the robot soccer set-up. The game is played in an indoor environment, inside a room that is totally obstructed from sunlight, and is lighted solely by multiple fluorescent lamps. The computer serves as the brain for the entire team of robots. It is interfaced to an overhead camera that serves as the only sensing device for locating the robots and the ball.
We employ a lot of image processing techniques to locate the robots, and based on the perceived robot positions, an AI system calculates the next robot move. The instructions to the robots are sent via a wireless communication system, and the robots merely execute them.
Exploratory environment is indoor – room totally obstructed from sunlight
Multiple monochromatic light sources – fluorescent / fluoride lamps *
Colour Object Recognition (Recognition speed: < 33ms)

31. Continuous charge signal
Machine Vision System
HARDWARE OUTLINE
2D Digital Image
Camera
Frame Grabber
3D Scene
Optics (Lens)
Firewire camera
The vision system is comprised of a CCD camera that is interfaced to the computer via a frame grabber card. The digitized image is then processed by the computer to find the robot’s position and orientation.
Image Sensors
CID (Charge Injection Device)
CCD (Charge Coupled Device)
PDA (Photo Diode Array)
Emmitted light *
2-D Intensity Image
Continuous charge signal

32. Colour as the machine sees it
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.
Even worse, the colours captured tend to diminish due to the adverse effects of illumination
A Green object tends to appear more as a whitish yellow object under bright white illumination.

33. Illumination Conditions
Colour objects traversing the field under spatially varying illumination intensities
Dark
Other Factors:
Lens focus
Bright
Object rotation
Dim
Quantum electrical effects
Shadows
The problem that hounds all vision systems is the elimination of the effects of varying illumination intensities in the scene of traversal.
Presence of similar colours
We need to automatically compensate for the
effects of varying illumination intensities in
the scene of traversal *

34. Recent Developments To some extent, the algorithm can see in the dark
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

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

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