Transcript ICT619 Intelligent Systems

ICT619 Intelligent
Systems
Unit Coordinator:
Shamim Khan
Room 2.065 ECL Building (North Wing)
Phone: 9360 2801
Email: [email protected]
Unit aims
 to be aware of the rational behind the artificial
intelligence and soft computing paradigms with their
advantages over traditional computing
 to gain an understanding of the theoretical foundations
of various types of intelligent systems technologies to a
level adequate for achieving objectives as stated below
 to develop the ability to evaluate intelligent systems,
and in particular, their suitability for specific
applications
 to be able to manage the application of various tools
available for developing intelligent systems
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Unit delivery and learning
structure
 3 hours of lecture/workshop per week.
 Lecture/WS time will be spent discussing the relevant
topic after an introduction by the lecturer.
 Topic lecture notes will be available early in the week
 Students will be expected to have made use of the
topic reading material in advance for the topic to be
covered.
 Bringing up issues and questions for discussion are
strongly encouraged to create an interactive learning
environment.
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Resources and Textbooks
 Main text:
 Seven methods for transforming corporate data
into business intelligence V Dhar & R Stein
Prentice Hall 1997
 The main text to be supplemented by chapters/articles
from other books/journals/magazines as well as notes
provided by the unit coordinator.
 A list of recommended readings and other resources
will be provided for each topic.
 Unit website:
http://www.it.murdoch.edu.au/units/ICT619 will
enable access to unit reading materials and links to
other resources.
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Assessment
ACTIVITY
DUE
WEIGHT
Workshop
participation
Continuous
10%
Project
Week 13
35%
Closed-book
Exam
End of teaching
period
55%
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Topic schedule
 Topic 1:
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Topic 2:
Topic 3:
Topic 4:
Topic 5:
Topic 6:
Topic 7:
Topic 8:
Topic 9:
Introduction to Intelligent Systems:
Tools, Techniques and Applications
Rule-Based Expert Systems
Fuzzy Systems
Neural Computing
Genetic Algorithms
Case-based Reasoning
Data Mining
Intelligent Software Agents
Language Technology
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Topic 1: Introduction to Intelligent
Systems
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What is an intelligent system?
Significance of intelligent systems in business
Characteristics of intelligent systems
The field of Artificial Intelligence (AI)
The Soft Computing paradigm
An Overview of Intelligent System Methodologies
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Expert Systems
Fuzzy Systems
Artificial Neural Networks
Genetic Algorithms (GA)
Case-based reasoning (CBR)
Data Mining
Intelligent Software Agents
Language Technology
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What is an intelligent system?
 What is intelligence?
 Easier to define using characteristics, eg,
 Reasoning
 Learning
 Adaptivity
 A truly intelligent system adapts itself to deal
with changes in problems (automatic learning)
 Machine intelligence follows problem solving
processes similar to humans
 Intelligent systems display machine
intelligence, not necessarily self-adapting
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Intelligent systems in business
 Intelligent systems in business utilise one or more intelligence
tools to aid decision making
 Provides business intelligence to
 Increase productivity
 Gain competitive advantage
 Examples of business intelligence – information on
 Customer behaviour patterns
 Market trend
 Efficiency bottlenecks
 Examples of successful intelligent systems applications in
business:
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Customer service
Scheduling
data mining
Financial market prediction
Quality control
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Intelligent systems in business –
some examples
 HNC software’s credit card fraud detector – 30-70%
improvement (ANN)
 MetLife insurance uses automated extraction of
information from applications (language technology)
 Personalized, Internet-based TV listings (intelligent
agent)
 Hyundai’s development apartment construction plans
(CBR)
 US Occupational Safety and Health Administration
(OSHA uses "expert advisors" to help identify fire and
other safety hazards at work sites (expert system).
Source: http://www.newsfactor.com/perl/story/16430.html
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Characteristics of intelligent
systems
 Possess one or more of these:
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Capability to extract and store knowledge
Human like reasoning process
Learning from experience (or training)
Dealing with imprecise expressions of facts
Finding solutions through processes similar to natural evolution
 Recent trend
 Interaction with user through
 natural language understanding
 speech recognition and synthesis
 image analysis.
 Most current intelligent systems based on
 rule based expert systems
 one or more of the methodologies belonging to soft computing
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The field of Artificial Intelligence
(AI)
 Primary goal:
 Development of software aimed at enabling machines to solve
problems through human-like reasoning
 Attempts to build systems based on a model of
knowledge representation and processing in the
human mind
 Encompasses study of the brain to understand its
structure and functions
 In existence as a discipline since the 1960s
 Failed to live up to initial expectations due to
 inadequate understanding of brain function
 complexity of problems to be solved
 Expert systems – an AI success story
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The Soft Computing (SC) paradigm
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Also known as Computational Intelligence
Unlike conventional computing, SC techniques
1. can be tolerant of imprecise, incomplete or corrupt input data
2. solve problems without explicit solution steps
3. learn the solution through repeated observation and
adaptation
4. can handle information expressed in vague linguistic terms
5. arrive at an acceptable solution through evolution
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The Soft Computing (SC) paradigm
(cont’d)
 The first four characteristics are common in
problem solving by humans
 The fifth characteristic (evolution) is common in
nature
 The predominant SC methodologies found in
current intelligent systems are:
 Artificial Neural Networks (ANN)
 Fuzzy Systems
 Genetic Algorithms (GA)
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Overview of Intelligent System
Methodologies
- Expert Systems (ES)
 Designed to solve problems in a specific
domain,
 eg, an ES to assist foreign currency traders
 Built by
 interrogating domain experts
 storing acquired knowledge in a form suitable for
problem solving problems using reasoning
 Used by
 Querying user for problem specific information
 Using the information to draw inferences from the
knowledge base
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Overview of Expert Systems (cont’d)
 Usual form of the expert system knowledge
base is a collection of IF … THEN … rules
 Some areas of ES application:
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banking and finance
manufacturing
retail
personnel management
emergency services
law
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Artificial Neural Networks (ANN)
 Human brain consists of billions of highly interconnected
simple processing elements known as neurons
 ANNs are based on a simplified model of the neurons and
their operation
 ANNs usually learn from experience – repeated
presentation of example problems with corresponding
solutions
 The learning phase may or may not involve human
intervention
 The problem solving strategy developed remains implicit
and unknown to the user
 Particularly suitable for problems not prone to algorithmic
solutions, eg, pattern recognition, decision support
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Artificial Neural Networks (cont’d)
 Different models of ANNs depending on
 Architecture
 learning method
 other operational characteristics
 Good at pattern recognition and classification problems
 Major strength - ability to handle previously unseen,
incomplete or corrupted data
 Some application examples:
- explosive detection at airports
- character and signature recognition
- financial risk assessment
- optimisation and scheduling.
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Genetic Algorithms (GA)
 Belongs to a broader field known as evolutionary computation
 Solution obtained by evolving solutions through a process
consisting of
 survival of the fittest
 crossbreeding, and
 mutation
 A population of candidate solutions initialised (the chromosomes)
 New generation of solutions produced from the current population
using specific genetic operations
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Genetic Algorithms (cont’d)
 New generation of solutions produced from the current population
using
 crossover (splicing and joining two chromosomes) and
 bit mutation
 Fitness of newly evolved solution evaluated using a fitness
function
 The steps of solution generation and evaluation continue until an
acceptable solution is found
 GAs have been used in
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portfolio optimisation
bankruptcy prediction
financial forecasting
fraud detection
scheduling
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Fuzzy Systems
 Traditional logic is two-valued – any proposition
is either true or false
 Problem solving in real-life must deal with
partially true or partially false propositions
 Imposing precision may be difficult and lead to
less than optimal solutions
 Fuzzy systems handle imprecise information
by assigning degrees of truth
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Fuzzy Systems (cont’d)
 FS allow us to express knowledge in vague
linguistic terms
 Flexibility and power of fuzzy systems now well
recognised
 Some applications of fuzzy systems:
 Control of manufacturing processes
 appliances such as air conditioners and video
cameras
 In combination with other intelligent system
methodologies to develop hybrid fuzzy-expert,
neuro-fuzzy, or fuzzy-GA systems
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Case-based reasoning (CBR)
 CBR systems solve problems by making use of knowledge about
similar problems encountered in the past
 The knowledge used in the past is built up as a case-base
 CBR systems search case base for cases with attributes similar to
given problem
 Solution created by synthesizing similar cases, and adjusting to
cater for differences between given problem and similar cases
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Case-based reasoning (cont’d)
 CBR systems can improve over time by learning from
mistakes made with past problems
 Application examples:
 Utilisation of shop floor expertise in aircraft repairsLegal
reasoning
 Dispute mediation
 Data mining
 Fault diagnosis
 Scheduling
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Data mining
 The process of exploring and analysing data for
discovering new and useful information
 Huge volumes of mostly point-of-sale (POS) data are
generated or captured electronically every day, eg,
 data generated by bar code scanners
 customer call detail databases
 web log files in e-commerce etc.
 Organizations are ending up with huge amounts of
mostly day-to-day transaction data
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Data mining (cont’d)
 It is possible to extract useful information on market
and customer behaviour by “mine”-ing the data
 Such information may
 indicate important underlying trends and associations in
market behaviour, and
 help gain competitive advantage by improving marketing
effectiveness
 Techniques such as artificial neural networks and
decision trees have made it possible to perform data
mining involving large volumes of data.
 Growing interest in applying data mining in areas such
direct target marketing campaigns, fraud detection, and
development of models to aid in financial predictions
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Intelligent software agents (ISA)
 ISAs are computer programs that provide active
assistance to information system users
 Help users cope with information overload
 Act in many ways like a personal assistant to the user
by attempting to adapt to the specific needs of the user
 Capable of learning from the user as well as other
intelligent software agents
 Application examples:
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Data Collection and Filtering
Pattern Recognition
Event Notification
Data Presentation
Planning and Optimization
Rapid Response Implementation
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Language Technology (LT)
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“Application of knowledge about human language in computer-based
solutions” (Dale 2004)
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Communication between people and computers is an important aspect of
any intelligent information system
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Applications of LT:
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Natural Language Processing (NLP)
Speech recognition
Optical character recognition (OCR)
Handwriting recognition
Machine translation
Text summarisation
Speech synthesis
A LT-based system can be the front-end of information systems
themselves based on other intelligence tools
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