CC6052 Week 12 slides

7/30/2019 CC6052 Week 12 slides

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Week 12

Other intelligent systems

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7/30/2019 CC6052 Week 12 slides


T h e

d i s c i p l i n e s o f A I ( t

h e

r o o t s ) a n d t h e

a p p l i c a t i o n s

S o u r c e : T u r b a n

( 2 0 0 1 )

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This week

Last week – expertise and expert systems

Overview of concepts and technologies being used in the

development of intelligent systems

(Artificial) Neural Networks

Genetic algorithms

Fuzzy logic

Intelligent agents

Game theory

Brief introduction to the concepts, and how these ideas

are being applied in MSS contexts 3

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Artificial Neural Networks

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Neural networks - introduction

Artificial neural networks (ANN)

◦ system of programs and data structures

◦ “imitates” the operation of the human brain

(as so many new technologies are said to do...)

◦ theoretically involves many parallel processors

◦ in practice, neural networks are often simulated

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Neural network is initially trained

i.e. fed large amounts of training data

(inputs produce a known set of outputs)

Neural network uses rules learned from patterns

in the data to construct a hidden layer of logic

Hidden layer(s) processes inputs, classifying according

to the experience of the model

Neural networks - introduction

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Neural networks - structure

Input layer Hidden layer Output layer

Income

Debt

Age

Residence

Source: Laudon & Laudon, 2004

Goodcredit risk

Bad credit

risk

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Machine learning

Supervised mode

o uses inputs for which desired outputs are known e.g. a historical set of loan applications

o difference between desired and actual output used to correct weights on neural network

Unsupervised mode

o only the inputs are known!

o the network is self-organising

o outputs derived by the network may not be meaningful to the user...

For artificial neural networks identifying tanks…or not!

See: http://neil.fraser.name/writing/tank/

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http://neil.fraser.name/writing/tank/

http://neil.fraser.name/writing/tank/

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Classification problem

Man on the left

does match

Neural networks – template matching (1)

based on Aleksander & Morton (1991)

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Man on the left

does not match



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Does the man on the left

match the template?



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What are we trying to match?

Smile

Eyes

Hair

Tie

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Neural networks - benefits

Good at some tasks that people are good at

Suitable for solving unstructured & semi-structured problems

Pattern recognition , even from incomplete information

Classification , abstraction and generalisation In theory, processing can be in parallel for faster computations

Ability to adapt to new data: learning

Exhibit fault-tolerant behaviour

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Neural networks - limitations

Not good at tasks that people are not good at

Not suitable for basic data processing or conventional

arithmetic calculations (conventional computer system better)

Need a vast amount of data

Might not ‘learn’ what is expected

Limited to classification and pattern recognition

Lack of explanatory capabilities Not economically viable for parallel processing

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Neural networks - applications

Applications of neural networks include: ◦ oil exploration data analysis

◦ weather prediction

◦ interpretation of nucleotide sequences in biology labs

◦ identifying credit card fraud by spotting changes in patternsof customer spending behaviour, e.g. Visa

◦ credit card approval

◦ bankruptcy prediction

◦

Optical Character Recognition (OCR)◦ speech recognition

◦ stock market predictions

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Genetic Algorithms

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Genetic algorithms - introduction

Genetic algorithms also called

◦ adaptive computation

◦

evolutionary programming problem-solving techniques

conceptually based adaptation to environment

process of evolution

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programmed to work the way populations solve problems:

changing and re-organising their component parts using

reproduction

crossover mutation

solutions alter and combine

worst ones are discarded

better ones go on to produce even better solutions

Genetic algorithms - introduction

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Genetic algorithms - a simple example (1)

Example based on the board game ‘Mastermind’

Imagine we are trying to guess a 6-digit binary number

The number that has been set by our opponent is:001010

but we don’t know this

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We make guesses and get scores for what is right

e.g. 111101 scores 1 because one digit is correct,

but we don’t know which one

Then we guess again and get another score…

… how many guesses before we get it right?

There are 64 possible combinations…

With random guessing:

average 32 attempts to guess the right number… but it could take the full 64 guesses

How it could be done using genetic algorithms …?


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The secret number we are trying to guess is 001010

With GA approach, we present random guesses, say◦ A – 110100 (scores 1)

◦ B – 111101 (scores 1)

◦ C – 111111 (scores 2)◦ D – 000000 (scores 4)

◦ E – 011011 (scores 4)

◦ F – 101010 (scores 5)

◦ G – 010101 (scores 1)

Discard the lowest scoring guesses, A, B, C and G

Now we have D, E and F to use


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Try splits... first 2 digits from one parent , last four digits from the other

D and E

H – 010000 (scores 3)

I – 001011 (scores 5)

E and F

J - 011010 (scores 4)

K- 101011 (scores 4)

D and F

L – 100000 (scores 4)

M – 001010 (scores 6) – !


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Genetic algorithms - terminology

Set of instructions performed to solve a problem◦ ‘algorithm’

Each iteration◦ ‘generation’

Score calculated◦ ‘fitness function’

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Methods used to reproduce (generate new offspring)

are genetic operators and include:

splitting and mating is called crossover some genes from "mother", some genes from "father"

unique individual created with some inheritance from parents

(sometimes twins might occur – same pattern of 0s and 1s)

more rarely might also introduce mutation

(randomly changing one of the digits)

Genetic algorithms - applications

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Genetic algorithms - applications

Genetic algorithms have been applied to

◦ Large-scale combinatorial mathematical programming problems

◦ Dynamic process control

◦ Scheduling problems and production planning

◦ Transportation and routing problems◦ Optimisation of complex machine design

e.g. General Electric used GA to optimise jet turbine aircraft engine

design (each design change required changes in up to 100 variables)

◦ Producing police sketches of criminals

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Fuzzy Logic

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Fuzzy logic ◦ based on "degrees of truth”

◦ rather than "true or false" (1 or 0) Boolean logic

Can everything be described in binary terms?

◦ A philosophical question...

Yes? (true)

No? (false)

Sometimes? Maybe? It depends... ( fuzzy )

“Are we nearly there yet...?”

the answer could be just yes or no ...

...or it depends whether we have just left, are half-way

there, or expect to arrive in 5 minutes...

Fuzzy logic - introduction

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Fuzzy logic - introduction

In practice, data in use might be uncertain some state between true and false

Fuzzy logic

allows 1 (true) and 0 (false) as extreme cases of truth

encompasses various states of truth in between

e.g. cold, cool, warm, hot might have overlappingtemperature ranges…

room temperature is ‘cool’ is between 50ºF and 70ºF,so we can definitely say 60ºF - 67ºF is ‘cool’

but 70ºF is on the cusp between warm and cool (like the Grand Old Duke of York…)

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“If temperature cool or cold and humidity low while outdoor wind high

and outdoor temperature low, raise heat and humidity in the room.”

- example taken from Laudon & Laudon (2004)

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Cold Warm Cool Norm

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Fuzzy logic - example

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70o F degrees is the norm:

a comfortable temperature 29

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40o F degrees is too cold:

an uncomfortable temperature 30

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100o F degrees is too hot:

an uncomfortable temperature 31

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60o F degrees is cool:

but not an uncomfortable temperature 32

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80o F degrees is warm:

but not an uncomfortable temperature 33

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Is 90o F degrees warm or hot?34

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Is 55o F degrees cool or cold?35

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? ?

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Is 75o F degrees warm or the norm?

Is 65o F degrees cool or the norm? 36

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Fuzzy logic - applications

Fuzzy logic applied in Japan:◦ Sendai’s subway system used fuzzy logic controls to accelerate smoothly

◦ Mitsubishi Heavy Industries implemented fuzzy logic controls in air

conditioners (reducing power consumption by 20%)

◦ Auto-focus device in cameras relies on fuzzy logic

… and in the US ◦ A Wall Street firm uses a system based on fuzzy logic to select companies

for potential acquisition

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Intelligent Agents

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Intelligent agents - introduction

Intelligent agents are programs that...◦ work in the background without direct human intervention...

◦ perform specific, repetitive, and predictable tasks...

◦ for an individual user, business process, or software application...

◦ with some degree of independence

Agents use in-built/learned knowledge to

accomplish tasks/make decisions for the user

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Intelligent agents - levels

Level 0 - e.g. web browsers

◦ agents retrieve documents for user under direct orders

e.g. user specifies URL

Level 1 - search engines

◦ agents provide a user-initiated search facility

Level 2 - software agents

o maintain user’s profiles

o monitor Internet information

o notify users when relevant information is found

Level 3 - learning or truly intelligent agents

o have a learning and deductive component of user profiles to help a user

who cannot formalise a query or target for a search40

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Intelligent agents - applications (1)

Intelligent agents◦ can be programmed to make decisions based on user's

personal preferences e.g.

delete junk e-mail schedule appointments

travel over interconnected networks to find the cheapest

airfare

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Game Theory

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Game theory - introduction

Also called ‘Multi - person Decision Theory’

Analyses the decision-making process when there is

more than one decision-maker (player)

Each player’s outcome (or payoff) depends on the

actions taken by the other players

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Each player’s action depends on

o the actions available to each player

o each player's preferences about the outcomes

o each player's beliefs about which actions are available to each

player and how each player ranks the outcomes

o each player’s beliefs about other player's beliefs, etc.

Cutting the (delicious chocolate) cake - ‘cutter’ and ‘chooser’

o if the cutter makes one slice bigger than the other o the chooser will take the biggest slice!

o ...better to make slices as near equal as possible

Game theory - introduction

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Game Theory (1)

Prisoner’s Dilemma ◦ Art and Don are arrested for a crime

if Art confesses and incriminates Don

Art goes free , Don gets 5 years

if Art does not confess, but Don incriminates Art

Don goes free , Art gets 5 years

if both Art and Don confess and incriminate each other

they both get 4 years

if both Art and Don do not confess

they both get 2 years

◦ they are told the same thing but they cannot communicate…

◦ could they trust each other if they could communicate...?

◦ what does each decide to do?

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Game Theory (2)

Prisoner’s Dilemma

◦ Mutual co-operation gives the best outcome for

Art and Don (together as a group)

minimum total time spent in jail

◦ Any other outcome less good for the group

might be better for one , but worse for the other

might be worse for both

total jail time overall would be greater

◦ Selfish action (betrayal) gives the worst outcome for Art and Don (individually and together) 47

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Game Theory (3)


◦ if Art says nothing he’ll get 2 years if Don says nothing

he’ll get 5 years if Don confesses

◦ if Art talks, he might go free

but if Don talks as well, they’ll both get4 years

◦ if Don says nothing he’ll get 2 years if Art says nothing

he’ll get 5 years if Art confesses

◦ if Don talks, he might go free but if Art talks as well, they’ll both get 4

years

-2, -2 -5, 0

0, -5 -4, -4

Don

Art Ssh Talk

Ssh

Talk

Is it better to co-operate

or defect (betray)?48

G Th ( )

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-4 -5

-5 -8

Don

Art Ssh! Talk

Ssh!

Talk

win-win lose-win

win-lose lose-lose

Don

Art Ssh Talk

Ssh!

Talk

Payoff matrix in win-lose format Payoff matrix in penalty format


Game Theory (4)

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G Th ( )

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Prisoner’s Dilemma Variation:exchange of closed bags

o bag 1 should contain goods

o bag 2 should contain payment

If both bags are full: win-win

o What happens if one bag is empty?

o What happen if both bags are

empty?

win-win lose-win

win-lose lose-lose

Goods

Cash Full Empty

Full

Empty

Game Theory (5)

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G Th (6)

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Prisoner’s Dilemma Variation:

exchange of closed bags, but

played every month

o bag 1 should contain goods

o bag 2 should contain payment

If both bags are full: win-win

o What happens if one bag is empty?

o What happen if both bags are empty?

Introduces memory o What happened last time ?

o What will be your strategy this time ?

win-win lose-win

win-lose lose-lose

Goods

Cash Full Empty

Full

Empty

Game Theory (6)

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G Th l

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Game Theory - examples

Prisoner’s Dilemma (PD) and Iterated Prisoner’s Dilemma (IPD) can be applied:

◦ 2 salesmen selling to 2 client companies

◦ 2 military generals attacking/defending 2 locations

◦ 2 companies deciding whether to advertise competing products

◦ 2 political candidates seeking support from colleagues

(David Cameron and Nick Clegg as leaders of Con-Dem alliance)

All examples of two-player non-zero-sum games

Game theory can be applied to

o airline competition

o coalition formation to apply political pressure

o plant location

o product diversification

o to derive optimal pricing, competitive bidding strategies and making

investment decisions52

G Th t t i

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Game Theory: strategies

Strategies for playing IPD◦ Always co-operate

will be beaten but “nasty” strategies

◦ Always defect

greedy strategies do not do well long-term

◦ Tit-for-tat

start by co-operating, then copy opponent

◦ Spiteful

co-operates until opponent defects, then always defects◦ Mistrust

start by defecting, then copy opponent

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G Th t t i

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Successful strategies

o “nice ”

o does not defect before opponent does

o “retaliate ”

o punish defection - it does not pay to be too nice

o “forgive ” o will retaliate, but will then co-operate if opponent does

o avoids long-term revenge

o “non-envious ”

o not trying to out-score opponent Could ideas from IPD show how altruism evolved?

o Sometimes it is “selfish” to appear to be “nice”!

o Nice guys finish first...

Game Theory: strategies

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Game theory applications

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Game theory - applications

Game theory is

“uniquely qualified to make sense of the forces at work”

in relation to executive decision-making ,

i.e. “to the strategies of some actual corporations caught

up in conglomerate warfare”

McDonald (1970) from Davis (1997)

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Further reading

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Further reading

Aleksander I & Morton H (1991), An Introduction to NeuralComputing, Chapman & Hall

Davis, M, 1997, Game Theory: a non-technical introduction , Dover

Laudon, K. & Laudon, J., 2004, Management Information Systems ,8th ed., Pearson Prentice Hall

‘Other intelligent techniques’: chapter 10, pages 333-339 Turban E. & Aronson J.E., 2001, Decision Support Systems and

Intelligent Systems , 6th ed., Prentice Hall

Neural Computing (the basics): chapter 15, pages 605-621, 634-636

Neural Computing Applications: chapter 16, pages 651-661

Genetic algorithms: chapter 16, pages 664-671

Fuzzy Logic: chapter 16, pages 672-676

Look up ‘game theory’ and/or the ‘prisoner’s dilemma’ on the web 56

CC6052 Week 12 slides

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