Transcript C H A P T E R

Chapter 7:
Specialized
Information Systems
Topics:
Please turn your
cell phone off.
Artificial Intelligence
Expert Systems
Virtual Reality
Other Specialized Systems
Chapter 7.1
An Overview of
Artificial Intelligence
Key Terms
• Artificial intelligence
• Artificial intelligence
systems
• Intelligent behavior
• Perceptive system
• Expert system
• Robotics
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Vision systems
Natural language processing
Learning systems
Neural network
Genetic algorithm
Intelligent agent
Artificial Intelligence
 AI

The ability of computers to
mimic or duplicate the
functions of the human brain
 Mobile AI

http://www.artificial-life.com/
 Customer Service Agents

http://www.conversagent.com
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Artificial Intelligence Systems
 People, procedures, hardware, software,
data, and knowledge needed to develop
computer systems and machines that
demonstrate characteristics of “intelligence”
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Intelligent Behavior
The ability to
 learn from experience
 apply knowledge acquired from experience
 handle complex situations
 solve problems when important information is
missing
 determine what is important
 react quickly and correctly to a new situation
 And understand visual images
Perceptive System
an AI system that approximates human senses
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Perceptive System
 A system that approximates the way a human sees,
hears, and feels objects.
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Interesting Statistics
 It has been estimated that
computers that can exhibit
humanlike intelligence
(including musical and
artistic aptitude, creativity,
physical movement
physically, and emotional
responsiveness) require
processing power of 20
million billion calculations
per second (by the year
2030?).
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The Difference Between Natural
& Artificial Intelligence
Attributes
Use Sensors
Creativity and Imagination
Human
High
High
Machine
Low
Low
Learn from Experience
Adaptability
Access external information
High
High
High
Low
Low
Low
Make complex calculations
Low
High
Transfer information
Low
High
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The Major Branches of AI
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The Major Branches of AI
Expert Systems
 Hardware and software that stores
knowledge and makes inferences, similar to a
human expert
 Used in many business applications
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The Major Branches of AI
Robotics
 Mechanical or computer devices that perform
tasks that either require a high degree of
precision or are tedious or hazardous for
humans
 Contemporary robotics combines highprecision machine capabilities with
sophisticated controlling software
 Many applications of robotics exist today
 Research into robots is continuing
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The Major Branches of AI
Robotics
Robots can be used in situations that are hazardous or
inaccessible to humans. The Rover was a remotecontrolled robot used by NASA to explore the surface of 12
Mars.
The Major Branches of AI
Vision Systems
 The hardware and software that permit
computers to capture, store, and manipulate
visual images and pictures
 Used by the U.S. Justice Department to perform
fingerprint analysis
 Used for identifying people based on facial
features
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The Major Branches of AI
Natural Language Processing
 Processing that allows the computer to
understand and react to statements and
commands made in a “natural” language, such
as English
 Three levels of voice recognition
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Command: recognition of dozens to hundreds
of words
Discrete: recognition of dictated speech with
pauses between words
Continuous: recognition of natural speech
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The Major Branches of AI
Natural Language Processing
 Processing that allows the computer to
understand and react to statements and
commands made in a “natural” language, such
as English
 Three levels of voice recognition
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
Command: recognition of dozens to hundreds
of words
Discrete: recognition of dictated speech with
pauses between words
Continuous: recognition of natural speech
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The Major Branches of AI
Natural Language Processing
Dragon Systems’ Naturally Speaking 7 Essentials uses
continuous voice recognition, or natural speech, allowing
the user to speak to the computer at a normal pace
without pausing between words. The spoken words are
transcribed immediately onto the computer screen.
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The Major Branches of AI
Learning Systems
 A combination of software and hardware that
allows the computer to change how it
functions or reacts to situations based on
feedback it receives
 Learning systems software requires feedback
on the results of actions or decisions
 Feedback is used to alter what the system will
do in the future
 Java Whale Watcher
 20 Questions
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The Major Branches of AI
Neural Networks
 A computer system that can simulate the
functioning of a human brain
 The ability to retrieve information even if some
of the neural nodes fail
 Fast modification of stored data as a result of
new information
 The ability to discover relationships and trends
in large databases
 The ability to solve complex problems for which
all the information is not present
Face Detection
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Other Artificial Intelligence
Applications
 Genetic algorithm: an approach to solving
large, complex problems in which a number of
related operations or models change and evolve
until the best one emerges
 Intelligent agent: programs and a knowledge
base used to perform a specific task for a
person, a process, or another program
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Chapter 7.2
An Overview of Expert
Systems
Key Terms
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Expert system shell
Knowledge base
If-then statements
Fuzzy logic
Rule
Inference engine
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Backward chaining
Forward chaining
Explanation facility
Knowledge acquisition
facility
• Domain
• Knowledge engineer
• Knowledge user
Characteristics and Limitations
of an Expert System
 Can explain its reasoning or suggested
decisions
 Can display “intelligent” behavior
 Can draw conclusions from complex
relationships
 Can provide portable knowledge
 Can deal with uncertainty
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Characteristics and Limitations
of an Expert System
 Not widely used or tested
 Difficult to use
 Limited to relatively narrow problems
 Cannot readily deal with “mixed” knowledge
 Possibility of error
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Characteristics and Limitations
of an Expert System
 Cannot refine its own knowledge
 Difficult to maintain
 May have high development costs

Expert system shell

A collection of software packages and tools used to
develop expert systems
 Raises legal and ethical concerns
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Components of an Expert System
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Components of an Expert System
Knowledge Base
Stores all relevant
information, data, rules,
cases, and relationships
used by the expert
system.
Uses
•Rules
•If-then Statements
•Fuzzy Logic
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The Knowledge Base
 Stores all relevant information, data, rules, cases, and
relationships used by the expert system
 Assembling human experts
 Use of fuzzy logic

A special research area in computer science that allows
shades of gray and does not require everything to be
simple black/white, yes/no, or true/false
 Use of rules
 Conditional statement that links given conditions to actions
or outcomes
 E.g. if-then statements
 Use of cases
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Components of an Expert System
Inference Engine
Seeks information and
relationships from the
knowledge base and
provides answers,
predictions, and
suggestions the way a
human expert would.
Uses
•Backward Chaining
•Forward Chaining
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The Inference Engine
 Seeks information and relationships from the knowledge
base and provides answers, predictions, and
suggestions the way a human expert would
 Backward chaining

Starting with conclusions and working backward to the
supporting facts
 Forward chaining
 Starting with the facts and working forwards to the
conclusions
 Comparison of backward and forward chaining
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The Inference Engine
Figure 7.4: Rules for a Credit Application
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Components of an Expert System
Explanation Facility
Allows a user to
understand how the
expert system arrived at
certain conclusions or
results.
For example: it allows a
doctor to find out the logic
or rationale of the
diagnosis made by a
medical expert system
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The Explanation Facility
 Allows a user or decision maker to understand
how the expert system arrived at certain
conclusions or results
 For example: it allows a doctor to find out the
logic or rationale of the diagnosis made by a
medical expert system
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Components of an Expert System
Knowledge acquisition
facility
Provide convenient and
efficient means of
capturing and storing all
the components of the
knowledge base.
Acts as an interface
between experts and the
knowledge base.
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Components of an Expert System
User Interface
Specialized user interface
software employed for
designing, creating,
updating, and using
expert systems.
The main purpose of the
user interface is to make
the development and use
of an expert system
easier for users and
decision makers
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Expert Systems Development
Figure 7.6: Steps in the Expert System Development
Process
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Participants in Expert System
Development
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Participants in Expert System
Development
 Domain
 The area of knowledge addressed by the expert
system
 Domain Expert
 The individual or group who has the expertise or
knowledge one is trying to capture in the expert system
 Knowledge Engineer
 An individual who has training or expertise in the
design, development, implementation, and
maintenance of an expert system
 Knowledge User
 The individual or group who uses and benefits from the
expert system
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Chapter 7.3
Virtual Reality
Key Terms
• Virtual reality system
Virtual Reality System
 A system that enables one or more users to move
and react in a computer-simulated environment
www.worlds.com
secondlife.com
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Chapter 7.4
Other Specialized
Systems
Key Terms
• Game theory
• Informatics
Other Specialized Systems
 Game theory

The use of information systems to develop
competitive strategies for people,
organizations, or even countries.
 Informatics
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A specialized system that combines traditional
disciplines, such as science and medicine,
with computer systems and technology
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Questions?
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Interesting Statistics
 Average Pentium PC executes 100 megaflops
(millions of operations per second)
 FSU’s super computer can carry out 2.5 teraflops
(trillion operations per second)
 Fastest supercomputers in 2004
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IBM’s BlueGene/L - 70.72 teraflops
NASA’s Columbia - 51.87 teraflops
NEC’s Earth Simulator - 35.86 teraflops
 To achieve anything even approaching human
intelligence, a computer must carry out 100 teraflops

Example: Computer speech recognition
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Some Current Research
www.cyc.com
In 1984 AI Pioneer Doug Lenat began formalizing human common
sense and entering it into a computer program he named Cyc
(short for encyclopedia). Lenat’s goal was to develop a rational
computer program that could make independent assertions. He has
labored years to codify facts such as "Once people die, they stop
buying things." He uses a form of symbolic logic called "predicate
calculus" to classify and show the properties of information in a
standard way. Now, 19 years later, with over 600 person-years and
$60 million invested, the Cyc knowledge base contains over 3
million [rules] that the average person knows about the world, plus
about 300,000 terms or concepts – Lenat’s intelligent child is ready
to begin earning its keep.
What service can Cyc provide to businesses? “I see this more as a
power source rather than a single application.” Lenat states. “[For
any given application], you need common-sense knowledge and
domain knowledge. We are building in the common-sense knowledge.”
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Case Study: Transko and
Gensym
Complex volatile systems, such as manufacturing and production systems,
telecommunications systems, supply-chain systems, and distribution systems,
typically require technicians to continuously monitor them in order to safeguard
against unexpected problems. Failure to catch tell-tale signs of trouble, in some
cases, could lead to disaster. Take for example Transko, the company responsible
for delivering natural gas to over 20 million industrial, commercial and domestic
customers in the UK.
Transko maintains over 275,000 km of natural gas pipeline, comprising high
pressure national and regional transmission systems and lower pressure distribution
systems. Gas is pumped through the network by 24 compressor stations located
around the country. Each compressor station is staffed with a team of technicians
that monitor the pressure within the system watching for increases in pressure, that
could lead to explosions, or decreases in pressure which could indicate a leakage of
the poisonous gas.
Such work is tedious and tiring. The stream of data to monitor is continuously
varying with compensating adjustments needed with each fluctuation. Operators
can’t afford a lapse in concentration, since failure in the system would be disastrous.
This scenario is ripe for automation. Enter Gensym.
http://www.gensym.com/
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Case Study: IBM’s eLiza
IBM has launched project eLiza to automate many system administrator duties and save their
customers big bucks. Project eLiza is an ongoing effort to create servers that respond to
unexpected capacity demands and system glitches without human intervention. The goal: new
highs in reliability, availability and serviceability, and new lows in downtime and cost of ownership.
IBM has classified a system administrator’s duties into four areas: system configuration,
maintenance, security, and efficiency. By analyzing the details involved in each of these areas,
IBM has been able to automate many of these tasks in order to create servers that are “smart”
enough to care for themselves. The goal is to create severs that are:

Self configuring: the ability for servers to define themselves "on-the fly". This aspect of
self-managing means that new features, software, and servers can be dynamically added
to the enterprise infrastructure with no disruption of services.
 Self-healing: the ability to recover from a failing components by first detecting and
isolating the failed component, taking it off-line, fixing or isolating the failed component ,
and reintroducing the fixed or replacement component into service without any
application disruption.
 Self-protecting: the ability to define and manage the access from users to all the
resources within the enterprise, protect against unauthorized resource access, detect
intrusions and report these activities as they occur, and provide backup/recovery
capabilities which are as secure as the original resource management systems.
 Self-optimizing: the ability to efficiently maximize resource utilization to meet the end user
needs with no human intervention required
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Expert System
Characteristics
 Can explain their reasoning or suggested
decisions
 Can display “intelligent” behavior
 Can draw conclusions from complex
relationships
 Can provide portable knowledge
 Can deal with uncertainty
 Java Whale Watcher
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Expert Systems Development Alternatives
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