Succeeding with Technology 4 th ed> Software Solutions for Personal and Professional Gain > Please...
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Succeeding with Technology 4th ed> Software Solutions for Personal and Professional Gain >
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Software from Conception to Installation and Beyond
System Software Application Software Smart Software: Artificial Intelligence
Software Solutions for Personal and Professional Gain
Chapter 3
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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
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Artificial Intelligence AI refers to the science and engineering of
creating computer systems that simulate human thought and behavior.
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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.”
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Artificial Intelligence
Artificial Intelligence
Computational Intelligence
Neural Networks Fuzzy Systems Evolutionary
Computation
Conventional AI
Expert Systems
Case-based Reasoning
Bayesian Networks
Behavior-based AI
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Conventional Intelligence Relies on the
programmer to instill the software with logical functionality to solve problems.
Artificial Intelligence
Conventional AI
Expert Systems
Case-based Reasoning
Bayesian Networks
Behavior-based AI
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Expert Systems Expert systems
are created with the assistance of a human expert who provides subject-specific knowledge.
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Applications of Expert Systems
PROSPECTOR:Used by geologists to
identify sites for drilling or mining
PUFF:Medical system
for diagnosis of respiratory conditions
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Applications of Expert Systems
DESIGN ADVISOR:Gives advice to designers of
processor chips
MYCIN:Medical system for diagnosing blood
disorders. First used in 1979
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Applications of Expert Systems
DENDRAL: Used to identify the structure of chemical compounds.
First used in 1965
LITHIAN: Gives advice to archaeologists examining
stone tools
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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.
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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
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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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…
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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
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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
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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
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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
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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
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Bayesian network Is a graphical model that represents a set of
variables and their relationships and dependencies.
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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
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Example from Medical Diagnostics
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
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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
Example from Medical Diagnostics
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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
TuberculosisPresentAbsent
5.0095.0
XRay ResultAbnormalNormal
14.585.5
Tuberculosis or CancerTrueFalse
10.289.8
Lung CancerPresentAbsent
5.5094.5
DyspneaPresentAbsent
45.055.0
BronchitisPresentAbsent
45.055.0
Visit To AsiaVisitNo Visit
100 0
SmokingSmokerNonSmoker
50.050.0
Example from Medical Diagnostics
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Example from Medical Diagnostics
Further interviewing of the patient produces the finding “Smoking” is “Smoker” This information propagates through the network
TuberculosisPresentAbsent
5.0095.0
XRay ResultAbnormalNormal
18.581.5
Tuberculosis or CancerTrueFalse
14.585.5
Lung CancerPresentAbsent
10.090.0
DyspneaPresentAbsent
56.443.6
BronchitisPresentAbsent
60.040.0
Visit To AsiaVisitNo Visit
100 0
SmokingSmokerNonSmoker
100 0
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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
TuberculosisPresentAbsent
0.1299.9
XRay ResultAbnormalNormal
0 100
Tuberculosis or CancerTrueFalse
0.3699.6
Lung CancerPresentAbsent
0.2599.8
DyspneaPresentAbsent
52.147.9
BronchitisPresentAbsent
60.040.0
Visit To AsiaVisitNo Visit
100 0
SmokingSmokerNonSmoker
100 0
Example from Medical Diagnostics
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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
TuberculosisPresentAbsent
0.1999.8
XRay ResultAbnormalNormal
0 100
Tuberculosis or CancerTrueFalse
0.5699.4
Lung CancerPresentAbsent
0.3999.6
DyspneaPresentAbsent
100 0
BronchitisPresentAbsent
92.27.84
Visit To AsiaVisitNo Visit
100 0
SmokingSmokerNonSmoker
100 0
Example from Medical Diagnostics
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Behavior-based Is a methodology that
simulates intelligence by combining many semiautonomous modules. Each module has a
specific activity for which it is responsible.
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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.
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Computational Intelligence Sets up a system
whereby the software can develop intelligence through an iterative process.
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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.
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Neurone vs. Node
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ANNs – The basics ANNs incorporate the two fundamental
components of biological neural nets:
1. Neurones (nodes)
2. Synapses (weights)
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Fuzzy Logic Fuzzy logic is derived from
fuzzy set theory, which deals with reasoning that is approximate rather than precise.
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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.
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TRADITIONAL REPRESENTATION OF LOGIC
Slow Fast
Speed = 0 Speed = 1
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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 ]
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FUZZY LOGIC REPRESENTATION CONT.
Slowest FastestSlow Fast
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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.
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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.
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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 )
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FUZZY LOGIC IN CONTROL SYSTEMS
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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.
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“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
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The GA Cycle of Reproduction
reproduction
population evaluation
modification
discard
deleted members
parents
children
modifiedchildren
evaluated children
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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
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Reproduction
reproduction
population
parents
children
Parents are selected at random with selection chances biased in relation to chromosome evaluations.
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Chromosome Modification
modificationchildren
Modifications are stochastically triggered Operator types are:
Mutation Crossover (recombination)
modified children
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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
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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)
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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
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Deletion
Generational GA:entire populations replaced with each iteration
Steady-state GA:a few members replaced each generation
population
discard
discarded members
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Evolutionary Walking
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Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >
AI Applications Robotics Computer Vision
Handwriting RecognitionFace Recognition
Natural Language ProcessingSpeech Recognition
Intelligent Agents Artificial Creativity
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Robotics Robotics was first introduced
into our vocabulary by Czech playwright Karel Capek in his 1920’s play Rossum’s Universal Robots.
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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.
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Robotics Should Robots look like
humans?anthropomorphic or
humanoid robots
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Robotics Or should they look like
other animals?
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Robotics Or look like non-living
things?Unmanned Arial Vehicles
UAVs
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Robotics No matter their shape
Robots are there to help us
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Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >
Robotics No matter their shape
Robots are there to help us
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Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >
Robotics No matter their shape
Robots are there to help us
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Succeeding with Technology 4th ed > Software Solutions for Personal and Professional Gain >
Robotics No matter their shape
Robots are there to help us
Kismet consists of a head with large eyes with eyelids, bushy eyebrows, rubber lips, and floppy ears.
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Robotics No matter their shape
Robots are there to help usCog is a humanoid
robot.
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Robotics No matter their shape
Robots are there to help usSay Hi to Asimohttp://asimo.honda.com/
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Robotics And now I give you NAO
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Computer Vision Understanding Images
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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.
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Project Natal – Xbox 360 Milo – The Virtual Boy
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Chapter 3 Questions?