ICT619 Intelligent Systems

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ICT619 Intelligent Systems

• Unit Coordinator
• Shamim Khan
• Room 2.065 ECL Building (North Wing)
• Phone [phone]
• Email s.khan_at_murdoch.edu.au

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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/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.

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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 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

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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?
• 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
• Customer service
• Scheduling
• data mining
• Financial market prediction
• Quality control

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Intelligent systems in business some examples

• HNC softwares 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)
• Hyundais 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
• 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

• 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

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The Soft Computing (SC) paradigm (contd)

• 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 (contd)

• Usual form of the expert system knowledge base is
  a collection of IF THEN rules
• Some areas of ES application
• 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 (contd)

• 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 (contd)

• 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
• 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 (contd)

• 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 (contd)

• 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 (contd)

• 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
• Data Collection and Filtering
• Pattern Recognition
• Event Notification
• Data Presentation
• Planning and Optimization
• Rapid Response Implementation

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Language Technology (LT)

• 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)
• 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