Transcript ICT619 Intelligent Systems

ICT619 Intelligent
Systems
Unit Coordinator:
Graham Mann
Room 2.061 ECL Building
Phone: 9360 7270
Email: [email protected]
Unit aims
 to be aware of the rationale of 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 should make use of the topic reading material
in advance for the topic to be covered
 Bringing up issues and questions for discussion are
encouraged to create an interactive learning
environment (this is assessed).
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Resources and Textbooks
 Main text:
 Negnevitsky, M. Artificial Intelligence: A Guide to
Intelligent Systems, 2005. 2nd Edition.
 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 12
35%
Closed-book
Exam
Nov exams 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
 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
 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?
 Hard to define unless you list characteristics eg,
 Reasoning
 Learning
 Adaptivity
 A truly intelligent system adapts itself to deal with
changes in problems (automatic learning)
 Few machines can do that at present
 Machine intelligence has a computer follow problem
solving processes something like that in humans
 Intelligent systems display machine-level intelligence,
reasoning, often learning, not necessarily self-adapting
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Intelligent systems in business
 Intelligent systems in business utilise one or more intelligence
tools, usually 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 (Customer Relations Modelling)
Scheduling (eg Mine Operations)
Data mining
Financial market prediction
Quality control
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Intelligent systems in business –
some examples
 HNC (now Fair Isaac) software’s credit card fraud detector Falcon
offers 30-70% improvement over existing methods (an example of
a neural network).
 MetLife insurance uses automated extraction of information from
applications in MITA (an example of language technology use)
 Personalized, Internet-based TV listings (an intelligent agent)
 Hyundai’s development apartment construction plans FASTrak-Apt
(a Case Based Reasoning project)
 US Occupational Safety and Health Administration (OSHA uses
"expert advisors" to help identify fire and other safety hazards at
work sites (an 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
 More sophisticated Interaction with the user through
 natural language understanding
 speech recognition and synthesis
 image analysis
 Most current intelligent systems are 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 1956
 Failed to live up to initial expectations due to
 inadequate understanding of intelligence, brain function
 complexity of problems to be solved
 Expert systems – an AI success story of the 80s
 Case Based Reasoning systems - partial success
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The Soft Computing (SC) paradigm
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Also known as Computational Intelligence
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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 individual 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 solving
problems, using simple reasoning
 Used by
 Querying the user for problem-specific information
 Using the information to draw inferences from the knowledge
base
 Supplies answers or suggested ways to collect further inputs
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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
 Note: not IF statements in procedural code
 Some areas of ES application:
 banking and finance (credit assessment, project
viability)
 maintenance (diagnosis of machine faults)
 retail (suggest optimal purchasing pattern)
 emergency services (equipment configuration)
 law (application of law in complex scenarios)
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Artificial Neural Networks (ANN)
 Human brain consists of 100 billion densely 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 their corresponding solutions
 After learning the ANN is able to solve problems, even with newish
input
 The learning phase may or may not involve human intervention
(supervised vs unsupervised learning)
 The problem solving 'model' 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 (eg type of activation function)
 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
- face 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 is initialised (the
chromosomes)
 New generations of solutions are produced beginning with the
intial population, using specific genetic operations: selection,
crossover and mutation
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Genetic Algorithms (cont’d)
 Next generation of solutions produced from the current population
using
 crossover (splicing and joining peices of the solution from parents) and
 mutation (random change in the parameters defining the solution)
 The 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
design of jet engines
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 - using fuzzy logic
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Fuzzy Systems (cont’d)
 FL allow us to express knowledge in vague linguistic
terms
 Flexibility and power of fuzzy systems now well
recognised (eg simplification of rules in control systems
where imprecision is found)
 Some applications of fuzzy systems:
 Control of manufacturing processes
 appliances such as air conditioners, washing machines and
video cameras
 Used 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 the case-base for cases with attributes
similar to given problem
 A solution created by synthesizing similar cases, and adjusting to
cater for differences between given problem and similar cases
 Difficult to do well in practice, but very powerful if you can do it
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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:
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Utilisation of shop floor expertise in aircraft repairs
Legal 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 “mining" the data
 Note: This goes far beyond simple statistical analysis of numerical
data, to classification and analysis of non-numerical data
 Such information might
 reveal 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 (from "data warehouses").
 Growing interest in applying data mining in areas such direct
target marketing campaigns, fraud detection, and development of
models to aid in financial predictions, antiterrorism systems
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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:
 News and Email Collection,
Filtering and Management
 Online Shopping
 Event Notification
 Personal scheduling
 Online help desks, interactive characters
 Rapid Response Implementation
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Language Technology (LT)
 “[The] application of knowledge about human language in computerbased solutions” (Dale 2004)
 Communication between people and computers is an important
aspect of any intelligent information system
 Applications of LT:
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Natural Language Processing (NLP)
Knowledge Representation
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
Hi, I am Cybelle.
What is your name?
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For Next Week
 Get hold of the textbook
 Visit the library and find the section on
artificial intelligence, browse some titles
 Get onto the unit website, download and
read papers concerning Expert Systems
 We will study the theory and practice
developing a simple expert system
 Have a look at the AAAI Applications
webpage at
http://www.aaai.org/AITopics/html/applications.html
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