Title: ICT619 Intelligent Systems
1ICT619 Intelligent Systems
- Unit Coordinator
- Graham Mann
- Room 2.061 ECL Building
- Phone 9360 7270
- Email g.mann_at_murdoch.edu.au
2Unit 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
3Unit 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).
4Resources 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/magazi
nes 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/I
CT619 will enable access to unit reading
materials and links to other resources.
5Assessment
ACTIVITY DUE WEIGHT
Workshop participation Continuous 10
Project Week 12 35
Closed-book Exam Nov exams period 55
6Topic schedule
- Topic 1 Introduction to Intelligent Systems
Tools, Techniques and Applications - Topic 2 Rule-Based Expert Systems
- Topic 3 Fuzzy Systems
- Topic 4 Neural Computing
- Topic 5 Genetic Algorithms
- Topic 6 Case-based Reasoning
- Topic 7 Data Mining
- Topic 8 Intelligent Software Agents
- Topic 9 Language Technology
7Topic 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
8What 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
9Intelligent 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 - Customer service (Customer Relations Modelling)
- Scheduling (eg Mine Operations)
- Data mining
- Financial market prediction
- Quality control
10Intelligent systems in business some examples
- HNC (now Fair Isaac) softwares 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) - Hyundais 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
11Characteristics of intelligent systems
- Possess one or more of these
- 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
12The 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
13The Soft Computing (SC) paradigm
- Also known as Computational Intelligence
- Unlike conventional computing, SC techniques
- can be tolerant of imprecise, incomplete or
corrupt input data - solve problems without explicit solution steps
- learn the solution through repeated observation
and adaptation - can handle information expressed in vague
linguistic terms - arrive at an acceptable solution through evolution
14The Soft Computing (SC) paradigm (contd)
- 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)
15Overview 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
16Overview of Expert Systems (contd)
- 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)
17Artificial 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
18Artificial Neural Networks (contd)
- 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
19Genetic 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
20Genetic Algorithms (contd)
- 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
- portfolio optimisation
- bankruptcy prediction
- financial forecasting
- design of jet engines
- scheduling
21Fuzzy 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
22Fuzzy Systems (contd)
- 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
23Case-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
24Case-based reasoning (contd)
- CBR systems can improve over time by learning
from mistakes made with past problems - Application examples
- Utilisation of shop floor expertise in aircraft
repairs - Legal reasoning
- Dispute mediation
- Data mining
- Fault diagnosis
- Scheduling
25Data 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
26Data mining (contd)
- 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
27Intelligent 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
28Language Technology (LT)
- The application of knowledge about human
language in computer-based solutions (Dale 2004) - Communication between people and computers is an
important aspect of any intelligent information
system - Applications of LT
- 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
29For 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.htm
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