The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.

1. Cranfield University, 16th November 2005
Useful Techniques in
Artificial Intelligence
-
Introduction
PRESENTED BY: Dr WILL BROWNE
Cybernetics,
University of Reading
Whiteknights
Reading
UK

3. Picture of Lt Commander Data

4. This 1100 spin Bosch machine is incredibly
quiet and positively high-end. It has
everything you would expect to find on a
Bosch including exclusive features like
the 3D AquaSpa wash system with Fuzzy
Control.

5. Stanley
http://en.wikipedia.org/wiki/Darpa_grand_challenge
$2 million Prize awarded to Stanford Racing Team
Five teams completed the Grand Challenge; four of them
under the 10 hour limit. The Stanford Racing Team took the
prize with a winning time of 6 hours, 53 minutes.
The SRT software system employs a number of advanced
techniques from the field of artificial intelligence,
such as probabilistic graphical models and machine learning.
http://www.darpa.mil/grandchallenge/index.asp
http://www.darpa.mil/grandchallenge/gallery.asp

6. Aim
To introduce the field of artificial
intelligence,
so that it is possible to
Determine if an artificial intelligence
technique is useful for a problem
and be able to
Select an appropriate technique for
further investigation.

7. Objective
• Introduction to Artificial Intelligence
• Generic function of Artificial
Intelligence tools
• Review of major techniques
• Benefit and pitfalls of applying these
tools.

8. Contents
• Applications of Techniques
• Description of Artificial Intelligence
Field
• Function of Important Techniques
• Benefit and Pitfalls of Applying
Techniques
• Summary

9. Finance & Business
• Predict stock market trends
• Insurance/credit risk assessment
• Fraud detection

10. Industry
• Communication: mobile phone
ground station & satellite networks
• Scheduling of work, transport, crane
operations and so on
• Routing of computer networks.
INTELSAT operates a fleet of 19 satellites

11. Engineering
• Optimisation of route planning
• Design of complex structures
• Process optimisation

12. Control
• Domestic appliances, such as
Microwave ovens
• Traffic flows
• Aircraft flight manoeuvres

13. Academia
• Game playing, e.g., chess
• Robotic football
• Test problems, e.g., iterated
prisoner’s dilemma.

14. “Definition” of AI
Artificial :-
easily understood
Artificial Intelligence :-
whole concept can be discussed
Intelligence :-
easy to recognise
hard to define

15. Artificial
• Not Human, plant or animal
• Computer-based
(workstation, PC, parallel-computer
or Mac)
• Computer programs

16. Artificial Intelligence
• Enable computers to perceive,
reason and act.
• Do jobs that currently humans do
better.
• Artificial Intelligence is what
Artificial Intelligence researchers
study.

17. Intelligence
• Intelligence is the ability to store,
retrieve and act on data - efficiently
and effectively.
• Intelligence has insight and can go
beyond problem definition - but not
experience?
• True intelligence does not exist!
“How do you speak ‘Alien’?”

18. Programme Languages
• Assembler
• C, C++, Java and FORTRAN
• Lisp, Small Talk and PROLOG
• Shells, e.g., G2 Expert System
• Toolboxes, e.g., Neural Networks in
Matlab.

19. Function
NOT RELIANT UPON
MATHEMATICAL DESCRIPTION
OF DOMAIN.
(stochastic)
• May include mathematics within
technique
• May be similar to mathematical
techniques

20. Functionality
Search Optimisation
Modelling
Knowledge-handling
Routing Scheduling
Visualisation Design
Querying Learning
Game-playing Adaptive-Control
Rule-Induction
Data-Access Data-Manipulation
Prediction Diagnosis

21. Function Summary
EXPLORE v EXPLOIT
EFFICIENTLY AND EFFECTIVELY

22. Functional Division of AI
Modelling -- Explore
Knowledge-Based -- Exploit
Optimisation -- Explore then
Exploit
Advanced -- Explore &
Exploit

23. Theoretical Division of AI
ARTIFICIAL INTELLIGENCE TECHNIQUES
LEARNING
GENETIC EVOLUTIONARY COMPUTATION NEURAL NETWORKS
LEARNING CLASSIFIER SYSTEMS
INTELLIGENT AGENTS
(inc. Artificial Life)
IMMUNE
SYSTEMS
CELLULAR
AUTOMATA
KNOWLEDGE BASED
Expert
Systems
Decision
Support
ENUMERATIVES
NON-GUIDED GUIDED
Backtracking Branch &
Bound
Dynamic
Programming
Case Based
Reasoning
FUZZY LOGIC
GUIDED
NON-GUIDED
Las Vegas
Tabu
Search Simulated
Annealing
Hopfiled Kohonen
GENETIC ALGORITHMS GENETIC
PROGRAMMING
EVOLUTION STRATEGIES
& PROGRAMMING
Maps
Multilayer
Perceptrons
ANT
COLONY
HILL CLIMBING
REINFORCEMENT LEARNING
STATE-BASED

24. Knowledge-Based:
Expert Systems
What: Capture and reason about knowledge
(especially human) in a transparent form.
How: Store of rules and information (the
knowledge base)
Reason about information (inference
engine).
Where: Rolling Mill Expert System project.
Satellite control/maintenance.
IF Temp < 400 oC THEN Rolling is Poor

25. Knowledge-Based:
Case Based Reasoning (CBR)
What: Past examples (cases) used to reason
about novel examples.
How: Store of cases and information
Reason and interpolate information
Update, maintain and repair cases.
Where: Decision support type systems.
Initial bridge design selection.
Temp
400 oC
Rolling
Poor
Temp
450 oC
Rolling
Good
Temp
430 oC
Rolling
?

26. Enumerative:
Branch & Bound
What: Knowledge stored in decision trees.
E.g., ID3 and C4.5
How: Domain is classified into sections
Tree of decisions is formed.
Where: Insurance fraud detection
Credit assessment.
Age > 25
T F
Sex = F
T F T F
250 300 300 425

27. Fuzzy Logic
What: Grey or fuzzy (i.e. human) thinking in
computers.
How: Member sets formed to classify inputs
Overlap of sets allows imprecise logic.
Where: Domestic appliance ‘intelligence’,
e.g., washing machines & microwaves.
Distribution
in
department F M
5.2 5.6 5.10 6.2
Height

28. Fuzzy Logic
What: Grey or fuzzy (i.e. human) thinking in
computers.
How: Member sets formed to classify inputs
Overlap of sets allows imprecise logic.
Where: Domestic appliance ‘intelligence’,
e.g., washing machines & microwaves.
2 4 6 8
Weight
Detergent :
Water ratio
Silk Wool

29. Learning:
Guided Search
What: Optimisation techniques that avoid
being trapped in local optima.
How: Simulated Annealing
Probability of accepting new search point
Probability reduced near to optimum.
How: Tabu Search
Can not search previously visited point
Therefor will not become stuck.
Where: Optimisation problems, where
domain is described by a function.
http://www.exatech.com/Optimization/optimization.htm

30. Learning:
Genetic Evolutionary Computation
What: Uses evolution to optimise fitness
(function) of solution.
How:
1. Population of solutions created
2. Fitness of each solution evaluated
3. Best solutions mated for new
population
4. Repeated until optimum solution.
Where: Design optimisation
Stock market investment
Autonomous programme development

31. Learning:
Genetic Evolutionary Computation
Genetic Algorithms:
Optimise numeric solution of fitness
function.
Learning Classifier Systems:
Optimise the co-operation of rules for
solving and input/output thickness
function.
Genetic Programming:
Optimise the interaction of code to
solve a programming function.
Evolutionary Systems:
Optimise the solution based on a
behavioural (phenotypic) instead of
genetic (genotypic) level.

32. F(x) = cos(x) + sin(x2) : 1 < x< 3
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
1 1.5 2 2.5 3
GA: j1 = 00010001
j2 = 01110001
j3 = 10010101
GP: j1 = sin(x) + 2sin(x2)
j2 = sin(x) + 2sin(x)cos(x)
j3 = sin(x) - 2sin(x)cos(x)

33. Intelligent-Agents:
Cellular Automata
What: Autonomous individuals (cells)
reacting to state of neighbouring
individuals - governed by rules.
How: Grid of individuals initiated
Behaviour rules introduced
(e.g., if > 3 neighbours on, then on)
Iteration until stable pattern emerges.
Where: Cast and mould design
Screensavers!

34. Neural Networks:
Back-Propagation
What: Mimic the function of the human
brain within a computer.
How: Nodes (representing neurons) are
linked to other nodes via connections
(representing synapses)
Nodes send messages to their output
(firing) when a threshold from their inputs
has been reached.
Where: Modelling of industrial systems
Speech recognition programs.
INPUTS OUTPUTS
INPUT
LAYER
HIDDEN
LAYER
OUTPUT
LAYER
NODE
CONNECTION

35. Neural Networks:
Self-Organising-Maps
What: Mimic the function of the human
brain within a computer. To determine
input relations (instead of input-output
relationships).
How: Nodes are linked to other nodes via
connections
Network of nodes autonomously adjusts to
represent input patterns.
Where: Fault diagnosis of industrial systems
Growing patterns in crops

36. Technique Selection
Overall Strategy - Explore (search) or
Exploit (optimise)
Representation - Required
transparency
Learning - Domain / fitness
function known?
Supervision - Feedback from
domain available?

37. No Free Lunch Theorem
“...all algorithms that search
for an extreme of a cost
function perform exactly the
same, according to any
performance measures,
when averaged over all
possible cost functions.”
[Wolpert and Macready 96]

38. No Free Lunch Theorem
Reasons why theorem does not hold in
practical situations:
• Inclusion of domain knowledge
• Co-adaptation algorithms
• Domain specific algorithms
• Non-infinite populations
• Resampling is important
• Representation style is important in
specific domains
[Wilson 97]

39. Interpolate & Extrapolate
• Aliasing
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0 1 2 3
• Incomplete picture
Learnt
Actual
x
x
x
x
0
0.7 1.2 1.7 2.2 2.7 xxxxx x x
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
-2

40. Garbage In = Garbage Out
• Often blind acceptance of inputs
• Often blind generation of outputs
• Practical need to:
Verify
Validate
Test

42. Lack of Transparency
• “Black Box” techniques, such as
Neural Networks
• Semi-transparent techniques, such as
Branch & Bound, become difficult
for human interpretation with large
problems
• Transparent techniques, such as
Expert Systems, become difficult for
human interpretation with very large
problems - above 1000 rules, the
logic chain becomes huge.

43. Benefits
• Not reliant upon the mathematical
description of the domain
• Speed, efficient solution production
• New/novel answers, effective
solutions produced
• Direct areas of further research
(human or conventional techniques)
• Hybridisation of techniques is
possible
• Cost, wide range of options available

44. Conclusion
• Useful tools to complement existing
techniques
• Multiple uses from exploring to
exploiting the domains of problems
• Beneficial in efficiently and
effectively obtaining solutions to
problems