succeeding with technology 4 th ed> software solutions for personal and professional gain >...

Succeeding with Technology 4th ed> Software Solutions for Personal and Professional Gain >

Please discontinue use of cell phoneand turn off the ringer.

Software from Conception to Installation and Beyond

System Software Application Software Smart Software: Artificial Intelligence

Software Solutions for Personal and Professional Gain

Chapter 3

Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >

Key Terms Artificial Intelligence Turing Test Conventional AI Computational Intelligence Expert System Neural Network Fuzzy Logic Genetic Algorithm

Smart Software: AI

Robotics Computer vision Natural language processing Speech recognition Intelligent Agent Virtual reality


Artificial Intelligence AI refers to the science and engineering of

creating computer systems that simulate human thought and behavior.


Turing Test

“A human judge engages in a natural language conversation with two other parties, one human, the other a machine; if the judge cannot reliably tell which is which, then the machine is said to pass the test.”


Artificial Intelligence


Computational Intelligence

Neural Networks Fuzzy Systems Evolutionary

Computation

Conventional AI

Expert Systems

Case-based Reasoning

Bayesian Networks

Behavior-based AI


Conventional Intelligence Relies on the

programmer to instill the software with logical functionality to solve problems.


Conventional AI

Expert Systems

Case-based Reasoning

Bayesian Networks

Behavior-based AI


Expert Systems Expert systems

are created with the assistance of a human expert who provides subject-specific knowledge.


Applications of Expert Systems

PROSPECTOR:Used by geologists to

identify sites for drilling or mining

PUFF:Medical system

for diagnosis of respiratory conditions



DESIGN ADVISOR:Gives advice to designers of

processor chips

MYCIN:Medical system for diagnosing blood

disorders. First used in 1979



DENDRAL: Used to identify the structure of chemical compounds.

First used in 1965

LITHIAN: Gives advice to archaeologists examining

stone tools


Components of an Expert System

The knowledge base is the collection of facts and rules which describe all the knowledge about the problem domain

The inference engine is the part of the system that chooses which facts and rules to apply when trying to solve the user’s query

The user interface is the part of the system which takes in the user’s query in a readable form and passes it to the inference engine. It then displays the results to the user.


Wumpus World Sescription Performance measure

gold +1000, death -1000 -1 per step, -10 for using the arrow

Environment Squares adjacent to wumpus are smelly Squares adjacent to pit are breezy Glitter if gold is in the same square Shooting kills wumpus if you are facing it Shooting uses up the only arrow Grabbing picks up gold if in same square Releasing drops the gold in same square

Sensors: Stench, Breeze, Glitter, Bump, Scream Actuators: Left turn, Right turn, Forward, Grab, Release, Shoot


Exploring a wumpus world


The Limitations of Rules The success of rule-based expert systems is due to

several factors: They can mimic some human problem-solving

strategies Rules are a part of everyday life, so people can relate

to them

However, a significant limitation is the knowledge elicitation bottleneck Experts may be unable to articulate their expertise

Heuristic knowledge is particularly difficult Experts may be too busy…


Problems with Expert Systems

Limited domain Systems are not always up to date,

and don’t learn No “common sense” Experts needed to setup and maintain

system


Another Way We Solve Problems?

By remembering how we solved a similar problem in the past

This is Case Based Reasoning (CBR) memory-based problem-solving re-using past experiences

Experts often find it easier to relate stories about past cases than to formulate rules


24

Problems we solve this way

Medicine doctor remembers previous patients especially for rare

combinations of symptoms Law

English/US law depends on precedence case histories are consulted

Management decisions are often based on past rulings

Financial performance is predicted by past results

© The Robert Gordon University, Aberdeen


Case-based Reasoning AI software maintains a library of problem

cases and solutions when confronted by a new problem

adjusts and applies a relevant previous solution to the new problem.

Retrieve Reuse Revise Retain

Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >© The Robert Gordon University, Aberdeen

26

Retain Review

Adapt

Retrieve

Database

NewProblem

Similar

SolutionCBR Solving Problems


Bayesian network Is a graphical model that represents a set of

variables and their relationships and dependencies.


Example from Medical Diagnostics

Network represents a knowledge structure that models the relationship between medical difficulties, their causes and effects, patient information and diagnostic tests

Visit to Asia

Tuberculosis

Tuberculosisor Cancer

XRay Result Dyspnea

BronchitisLung Cancer

Smoking

Patient Information

Medical Difficulties

Diagnostic Tests



Relationship knowledge is modeled by deterministic functions, logic and conditional probability distributions

Patient Information

Diagnostic Tests

Visit to Asia

Tuberculosis

Tuberculosisor Cancer

XRay Result Dyspnea

BronchitisLung Cancer

SmokingTuber

Present

Present

Absent

Absent

Lung Can

Present

Absent

Present

Absent

Tub or Can

True

True

True

False

Medical DifficultiesTub or Can

True

True

False

False

Bronchitis

Present

Absent

Present

Absent

Present

0.90

0.70

0.80

0.10

Absent

0.l0

0.30

0.20

0.90

Dyspnea


Propagation algorithm processes relationship information to provide an unconditional or marginal probability distribution for each node

The unconditional or marginal probability distribution is frequently called the belief function of that node

TuberculosisPresentAbsent

1.0499.0

XRay ResultAbnormalNormal

11.089.0

Tuberculosis or CancerTrueFalse

6.4893.5

Lung CancerPresentAbsent

5.5094.5

DyspneaPresentAbsent

43.656.4

BronchitisPresentAbsent

45.055.0

Visit To AsiaVisitNo Visit

1.0099.0

SmokingSmokerNonSmoker

50.050.0



As a finding is entered, the propagation algorithm updates the beliefs attached to each relevant node in the network

Interviewing the patient produces the information that “Visit to Asia” is “Visit” This finding propagates through the network and the belief functions of several

nodes are updated


5.0095.0


14.585.5


10.289.8


5.5094.5


45.055.0


45.055.0


100 0


50.050.0




Further interviewing of the patient produces the finding “Smoking” is “Smoker” This information propagates through the network


5.0095.0


18.581.5


14.585.5


10.090.0


56.443.6


60.040.0


100 0


100 0


Finished with interviewing the patient, the physician begins the examination The physician now moves to specific diagnostic tests such as an X-Ray, which

results in a “Normal” finding which propagates through the network Note that the information from this finding propagates backward and forward

through the arcs


0.1299.9


0 100


0.3699.6


0.2599.8


52.147.9


60.040.0


100 0


100 0



The physician also determines that the patient is having difficulty breathing, the finding “Present” is entered for “Dyspnea” and is propagated through the network

The doctor might now conclude that the patient has bronchitis and does not have tuberculosis or lung cancer


0.1999.8


0 100


0.5699.4


0.3999.6


100 0


92.27.84


100 0


100 0



Behavior-based Is a methodology that

simulates intelligence by combining many semiautonomous modules. Each module has a

specific activity for which it is responsible.


Subsumption Architecture

Behavior D

Behavior C

Behavior B

Behavior A

Sensor 3

Sensor 2

Sensor 1

Actuators

Sensor 0

Behavior C

Behavior BS

i

i

SS

Suppressor node: eliminates lower level control signal and replaces it with one from higher level. Suppression onlyoccurs when higher level is active. Inhibitor node: eliminates lower level control signal withoutany substitution

From p 94, Robot Programming, A Practical Guide to BB Robotics, Joseph L. Jones.


Computational Intelligence Sets up a system

whereby the software can develop intelligence through an iterative process.


Neural Network A neural network simulates

the functioning of the neurons in a human brain in software.

A neural network is loaded with a random program, and the output is measured against a desired output, which positively or negatively affects the pathways between neurons.


Neurone vs. Node


ANNs – The basics ANNs incorporate the two fundamental

components of biological neural nets:

1. Neurones (nodes)

2. Synapses (weights)


Fuzzy Logic Fuzzy logic is derived from

fuzzy set theory, which deals with reasoning that is approximate rather than precise.


WHAT IS FUZZY LOGIC?

Definition of fuzzy

Fuzzy – “not clear, distinct, or precise; blurred”

Definition of fuzzy logic

A form of knowledge representation suitable for

notions that cannot be defined precisely, but

which depend upon their contexts.


TRADITIONAL REPRESENTATION OF LOGIC

Slow Fast

Speed = 0 Speed = 1


FUZZY LOGIC REPRESENTATION

For every problem must represent in terms of fuzzy sets.

What are fuzzy sets?

Slowest

Fastest

Slow

Fast

[ 0.0 – 0.25 ]

[ 0.25 – 0.50 ]

[ 0.50 – 0.75 ]

[ 0.75 – 1.00 ]


FUZZY LOGIC REPRESENTATION CONT.

Slowest FastestSlow Fast


ORIGINS OF FUZZY LOGIC

Traces back to Ancient Greece

Lotfi Asker Zadeh ( 1965 )

First to publish ideas of fuzzy logic.

Professor Toshire Terano ( 1972 )

Organized the world's first working group on fuzzy

systems.

F.L. Smidth & Co. ( 1980 )

First to market fuzzy expert systems.


FUZZY LOGIC VS. NEURAL NETWORKS

How does a Neural Network work?

Both model the human brain.

Fuzzy Logic

Neural Networks

Both used to create behavioral systems.


FUZZY LOGIC IN CONTROL SYSTEMS

Fuzzy Logic provides a more efficient and

resourceful way to solve Control Systems.

Some Examples

Temperature Controller

Anti – Lock Break System ( ABS )


FUZZY LOGIC IN CONTROL SYSTEMS

http://www.youtube.com/watch?v=cFcQpC8vAr8&feature=related


Genetic Algorithm A genetic algorithm is a form of evolutionary

computation that is used to solve large, complex problems where a number of algorithms or models change and evolve until the best one emerges.


“Genetic Algorithms are good at taking large,

potentially huge search spaces and navigating

them, looking for optimal combinations of things, solutions you might not

otherwise find in a lifetime.”

- Salvatore Mangano

Computer Design, May 1995

Genetic Algorithms:A Tutorial


The GA Cycle of Reproduction

reproduction

population evaluation

modification

discard

deleted members

parents

children

modifiedchildren

evaluated children


Population

Chromosomes could be:Bit strings (0101 ... 1100)Real numbers (43.2 -33.1 ... 0.0 89.2) Permutations of element (E11 E3 E7 ... E1 E15)Lists of rules (R1 R2 R3 ... R22 R23)Program elements (genetic programming) ... any data structure ...

population


Reproduction

reproduction

population

parents

children

Parents are selected at random with selection chances biased in relation to chromosome evaluations.


Chromosome Modification

modificationchildren

Modifications are stochastically triggered Operator types are:

Mutation Crossover (recombination)

modified children


Mutation: Local ModificationBefore: (1 0 1 1 0 1 1 0)

After: (0 1 1 0 0 1 1 0)

Before: (1.38 -69.4 326.44 0.1)

After: (1.38 -67.5 326.44 0.1)

Causes movement in the search space(local or global)

Restores lost information to the population


Crossover: Recombination

P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1

P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2

Crossover is a critical feature of genetic

algorithms: It greatly accelerates search early in evolution of a

population It leads to effective combination of schemata (subsolutions

on different chromosomes)


Evaluation

The evaluator decodes a chromosome and assigns it a fitness measure

The evaluator is the only link between a classical GA and the problem it is solving

evaluation

evaluatedchildren

modifiedchildren


Deletion

Generational GA:entire populations replaced with each iteration

Steady-state GA:a few members replaced each generation

population

discard

discarded members


Evolutionary Walking

http://www.youtube.com/watch?v=KHV7fWvnn_0


AI Applications Robotics Computer Vision

Handwriting RecognitionFace Recognition

Natural Language ProcessingSpeech Recognition

Intelligent Agents Artificial Creativity


Robotics Robotics was first introduced

into our vocabulary by Czech playwright Karel Capek in his 1920’s play Rossum’s Universal Robots.


Robotics The word “robota” in Czech

means simply work. Robots as machines that resemble people, work tirelessly, and revolt against their creators

The same myth/concept is found in many books/movies today: “Terminator”, “Star-Wars” series. Mary Shelley’s 1818 Frankenstein.


Robotics Should Robots look like

humans?anthropomorphic or

humanoid robots


Robotics Or should they look like

other animals?


Robotics Or look like non-living

things?Unmanned Arial Vehicles

UAVs


Robotics No matter their shape

Robots are there to help us




http://www.youtube.com/watch?v=6NpNOt3oW8E&feature=related




Kismet consists of a head with large eyes with eyelids, bushy eyebrows, rubber lips, and floppy ears.



Robots are there to help usCog is a humanoid

robot.



Robots are there to help usSay Hi to Asimohttp://asimo.honda.com/

http://asimo.honda.com/

http://www.youtube.com/watch?v=kFgXEkzMq7A&feature=related


Robotics And now I give you NAO

http://www.youtube.com/watch?v=2STTNYNF4lk


Computer Vision Understanding Images


Virtual Reality Virtual reality is often

linked to artificial intelligence because it leans on AI technologies.

Immersive virtual reality refers to a simulation in which the user becomes fully immersed in an artificial, three-dimensional world that is completely generated by a computer.


Project Natal – Xbox 360 Milo – The Virtual Boy

http://www.youtube.com/watch?v=CPIbGnBQcJY


Chapter 3 Questions?

succeeding with technology 4 th ed> software solutions for personal and professional gain >...

Documents

ed software solutions

professional gain components

professional gain chapter

professional gain turing

human expert

computer systems

stone tools slide

medical system