ICT619 Intelligent ICT619 Intelligent SystemsSystemsUnit Coordinator:Unit Coordinator:Shamim KhanShamim KhanRoom 2.065 ECL Building (North Wing)Room 2.065 ECL Building (North Wing)Phone: 9360 2801 Phone: 9360 2801 Email: Email: s.khan@murdoch.edu.aus.khan@murdoch.edu.au

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Unit aimsUnit aims

to be aware of the rational behind the artificial to be aware of the rational behind the artificial intelligence and soft computing paradigms with their intelligence and soft computing paradigms with their advantages over traditional computingadvantages over traditional computing

to gain an understanding of the theoretical foundations to gain an understanding of the theoretical foundations of various types of intelligent systems technologies to a of various types of intelligent systems technologies to a level adequate for achieving objectives as stated belowlevel adequate for achieving objectives as stated below

to develop the ability to evaluate intelligent systems, to develop the ability to evaluate intelligent systems, and in particular, their suitability for specific and in particular, their suitability for specific applications applications

to be able to manage the application of various tools to be able to manage the application of various tools available for developing intelligent systemsavailable for developing intelligent systems

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Unit delivery and learning Unit delivery and learning structurestructure

3 hours of lecture/workshop per week. 3 hours of lecture/workshop per week. Lecture/WS time will be spent discussing the relevant Lecture/WS time will be spent discussing the relevant

topic after an introduction by the lecturer. topic after an introduction by the lecturer. Topic lecture notes will be available early in the weekTopic lecture notes will be available early in the week Students will be expected to have made use of the Students will be expected to have made use of the

topic reading material in advance for the topic to be topic reading material in advance for the topic to be covered. covered.

Bringing up issues and questions for discussion are Bringing up issues and questions for discussion are strongly encouraged to create an interactive learning strongly encouraged to create an interactive learning environment. environment.

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Resources and TextbooksResources and Textbooks

Main text:Main text: Seven methods for transforming corporate data Seven methods for transforming corporate data

into business intelligence into business intelligence V Dhar & R Stein V Dhar & R Stein Prentice Hall 1997Prentice Hall 1997

The main text to be supplemented by chapters/articles The main text to be supplemented by chapters/articles from other books/journals/magazines as well as notes from other books/journals/magazines as well as notes provided by the unit coordinator. provided by the unit coordinator.

A list of recommended readings and other resources A list of recommended readings and other resources will be provided for each topic.will be provided for each topic.

Unit website: Unit website: http://www.it.murdoch.edu.au/units/ICT619http://www.it.murdoch.edu.au/units/ICT619 will enable will enable access to unit reading materials and links to other access to unit reading materials and links to other resources. resources.

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AssessmentAssessment

ACTIVITYACTIVITY DUEDUE WEIGHTWEIGHTWorkshop Workshop participationparticipation

ContinuousContinuous 10%10%

ProjectProject Week 13Week 13 35%35%

Closed-book Closed-book ExamExam

End of teaching End of teaching periodperiod

55%55%

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Topic scheduleTopic schedule

Topic 1:Topic 1: Introduction to Intelligent Systems: Introduction to Intelligent Systems: Tools, Techniques and Applications Tools, Techniques and Applications

Topic 2:Topic 2: Rule-Based Expert Systems Rule-Based Expert Systems Topic 3:Topic 3: Fuzzy SystemsFuzzy Systems Topic 4:Topic 4: Neural ComputingNeural Computing Topic 5:Topic 5: Genetic AlgorithmsGenetic Algorithms Topic 6:Topic 6: Case-based Reasoning Case-based Reasoning Topic 7:Topic 7: Data MiningData Mining Topic 8:Topic 8: Intelligent Software AgentsIntelligent Software Agents Topic 9:Topic 9: Language TechnologyLanguage Technology

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Topic 1: Introduction to Intelligent Topic 1: Introduction to Intelligent SystemsSystems

What is an intelligent system?What is an intelligent system? Significance of intelligent systems in business Significance of intelligent systems in business Characteristics of intelligent systemsCharacteristics of intelligent systems The field of Artificial Intelligence (AI)The field of Artificial Intelligence (AI) The Soft Computing paradigmThe Soft Computing paradigm An Overview of Intelligent System MethodologiesAn Overview of Intelligent System Methodologies

Expert SystemsExpert Systems Fuzzy SystemsFuzzy Systems Artificial Neural NetworksArtificial Neural Networks Genetic Algorithms (GA) Genetic Algorithms (GA) Case-based reasoning (CBR) Case-based reasoning (CBR) Data MiningData Mining Intelligent Software Agents Intelligent Software Agents Language TechnologyLanguage Technology

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What is an intelligent system?What is an intelligent system?

What is intelligence?What is intelligence? Easier to define using characteristics, eg,Easier to define using characteristics, eg,

ReasoningReasoning LearningLearning AdaptivityAdaptivity

A truly intelligent system adapts itself to deal A truly intelligent system adapts itself to deal with changes in problems (automatic learning)with changes in problems (automatic learning)

Machine intelligence Machine intelligence follows problem solving follows problem solving processes similar to humansprocesses similar to humans

Intelligent systems display machine Intelligent systems display machine intelligence, not necessarily self-adaptingintelligence, not necessarily self-adapting

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Intelligent systems in businessIntelligent systems in business

Intelligent systems in business utilise one or moreIntelligent systems in business utilise one or more intelligence intelligence toolstools to aid decision making to aid decision making

Provides business intelligence to Provides business intelligence to Increase productivityIncrease productivity Gain competitive advantageGain competitive advantage

Examples of business intelligence – information onExamples of business intelligence – information on Customer behaviour patternsCustomer behaviour patterns Market trendMarket trend Efficiency bottlenecksEfficiency bottlenecks

Examples of successful intelligent systems applications in Examples of successful intelligent systems applications in business:business: Customer service Customer service Scheduling Scheduling data mining data mining Financial market prediction Financial market prediction Quality control Quality control

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Intelligent systems in business – Intelligent systems in business – some examplessome examples

HNC software’s credit card fraud detector – 30-70% HNC software’s credit card fraud detector – 30-70% improvement (ANN)improvement (ANN)

MetLife insurance uses automated extraction of MetLife insurance uses automated extraction of information from applications (language technology)information from applications (language technology)

Personalized, Internet-based TV listings (intelligent Personalized, Internet-based TV listings (intelligent agent)agent)

Hyundai’s development apartment construction plans Hyundai’s development apartment construction plans (CBR)(CBR)

US Occupational Safety and Health Administration US Occupational Safety and Health Administration (OSHA uses "expert advisors" to help identify fire and (OSHA uses "expert advisors" to help identify fire and other safety hazards at work sites (expert system). other safety hazards at work sites (expert system).

Source: http://www.newsfactor.com/perl/story/16430.htmlSource: http://www.newsfactor.com/perl/story/16430.html

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Characteristics of intelligent systemsCharacteristics of intelligent systems

Possess one or more of these:Possess one or more of these: Capability to extract and store knowledge Capability to extract and store knowledge Human like reasoning processHuman like reasoning process Learning from experience (or training)Learning from experience (or training) Dealing with imprecise expressions of factsDealing with imprecise expressions of facts Finding solutions through processes similar to natural evolutionFinding solutions through processes similar to natural evolution

Recent trend Recent trend Interaction with user through Interaction with user through

natural language understandingnatural language understanding speech recognition and synthesisspeech recognition and synthesis image analysis. image analysis.

Most current intelligent systems based on Most current intelligent systems based on

rule based expert systems rule based expert systems one or more of the methodologies belonging to soft computingone or more of the methodologies belonging to soft computing

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The field of Artificial Intelligence (AI)The field of Artificial Intelligence (AI)

Primary goal:Primary goal: Development of software aimed at enabling machines to solve Development of software aimed at enabling machines to solve

problems through human-like reasoningproblems through human-like reasoning Attempts to build systems based on a model of Attempts to build systems based on a model of

knowledge representation and processing in the human knowledge representation and processing in the human mindmind

Encompasses study of the brain to understand its Encompasses study of the brain to understand its structure and functionsstructure and functions

In existence as a discipline since the 1960sIn existence as a discipline since the 1960s Failed to live up to initial expectations due toFailed to live up to initial expectations due to

inadequate understanding of brain functioninadequate understanding of brain function complexity of problems to be solvedcomplexity of problems to be solved

Expert systems – an AI success storyExpert systems – an AI success story

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The Soft Computing (SC) paradigmThe Soft Computing (SC) paradigm

Also known as Computational IntelligenceAlso known as Computational Intelligence Unlike conventional computing, SC techniques Unlike conventional computing, SC techniques

1.1. can be tolerant of imprecise, incomplete or corrupt input datacan be tolerant of imprecise, incomplete or corrupt input data

2.2. solve problems without explicit solution stepssolve problems without explicit solution steps

3.3. learn the solution through repeated observation and learn the solution through repeated observation and adaptationadaptation

4.4. can handle information expressed in vague linguistic termscan handle information expressed in vague linguistic terms

5.5. arrive at an acceptable solution through evolutionarrive at an acceptable solution through evolution

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The Soft Computing (SC) paradigm The Soft Computing (SC) paradigm (cont’d)(cont’d)

The first four characteristics are common in The first four characteristics are common in problem solving by humansproblem solving by humans

The fifth characteristic (evolution) is common in The fifth characteristic (evolution) is common in naturenature

The predominant SC methodologies found in The predominant SC methodologies found in current intelligent systems are: current intelligent systems are: Artificial Neural Networks (ANN)Artificial Neural Networks (ANN) Fuzzy SystemsFuzzy Systems Genetic Algorithms (GA)Genetic Algorithms (GA)

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Overview of Intelligent System Overview of Intelligent System MethodologiesMethodologies- Expert Systems (ES)- Expert Systems (ES)

Designed to solve problems in a specific Designed to solve problems in a specific domain, domain, eg, an ES to assist foreign currency traderseg, an ES to assist foreign currency traders

Built by Built by interrogating domain expertsinterrogating domain experts storing acquired knowledge in a form suitable for storing acquired knowledge in a form suitable for

problem solving problems using reasoningproblem solving problems using reasoning Used byUsed by

Querying user for problem specific informationQuerying user for problem specific information Using the information to draw inferences from the Using the information to draw inferences from the

knowledge baseknowledge base

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Overview of Expert Systems Overview of Expert Systems (cont’d)(cont’d)

Usual form of the expert system knowledge Usual form of the expert system knowledge base is a collection of IF … THEN … rules base is a collection of IF … THEN … rules

Some areas of ES application:Some areas of ES application: banking and financebanking and finance manufacturingmanufacturing retailretail personnel managementpersonnel management emergency servicesemergency services lawlaw

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Artificial Neural Networks (ANN)Artificial Neural Networks (ANN)

Human brain consists of billions of highly interconnected Human brain consists of billions of highly interconnected simple processing elements known as neuronssimple processing elements known as neurons

ANNs are based on a simplified model of the neurons and ANNs are based on a simplified model of the neurons and their operationtheir operation

ANNs usually learn from experience – repeated ANNs usually learn from experience – repeated presentation of example problems with corresponding presentation of example problems with corresponding solutionssolutions

The learning phase may or may not involve human The learning phase may or may not involve human intervention intervention

The problem solving strategy developed remains implicit The problem solving strategy developed remains implicit and unknown to the userand unknown to the user

Particularly suitable for problems not prone to algorithmic Particularly suitable for problems not prone to algorithmic solutions, eg, pattern recognition, decision supportsolutions, eg, pattern recognition, decision support

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Artificial Neural Networks (cont’d)Artificial Neural Networks (cont’d)

Different models of ANNs depending on Different models of ANNs depending on ArchitectureArchitecture learning methodlearning method other operational characteristicsother operational characteristics

Good at pattern recognition and classification problemsGood at pattern recognition and classification problems Major strength - ability to handle previously unseen, Major strength - ability to handle previously unseen,

incomplete or corrupted dataincomplete or corrupted data Some application examples: Some application examples:

- explosive detection at airports- explosive detection at airports- character and signature recognition- character and signature recognition- financial risk assessment- financial risk assessment- optimisation and scheduling.- optimisation and scheduling.

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Genetic Algorithms (GA)Genetic Algorithms (GA)

Belongs to a broader field known as evolutionary computationBelongs to a broader field known as evolutionary computation

Solution obtained by evolving solutions through a process Solution obtained by evolving solutions through a process consisting ofconsisting of survival of the fittestsurvival of the fittest crossbreeding, and crossbreeding, and mutationmutation

A population of candidate solutions initialised (the chromosomes)A population of candidate solutions initialised (the chromosomes)

New generation of solutions produced from the current population New generation of solutions produced from the current population using specific genetic operationsusing specific genetic operations

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Genetic Algorithms (cont’d)Genetic Algorithms (cont’d)

New generation of solutions produced from the current population New generation of solutions produced from the current population usingusing crossover (splicing and joining two chromosomes) and crossover (splicing and joining two chromosomes) and bit mutationbit mutation

Fitness of newly evolved solution evaluated using a fitness Fitness of newly evolved solution evaluated using a fitness functionfunction

The steps of solution generation and evaluation continue until an The steps of solution generation and evaluation continue until an acceptable solution is foundacceptable solution is found

GAs have been used in GAs have been used in portfolio optimisation portfolio optimisation bankruptcy predictionbankruptcy prediction financial forecastingfinancial forecasting fraud detectionfraud detection schedulingscheduling

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Fuzzy SystemsFuzzy Systems

Traditional logic is two-valued – any Traditional logic is two-valued – any proposition is either true or falseproposition is either true or false

Problem solving in real-life must deal with Problem solving in real-life must deal with partially true or partially false propositionspartially true or partially false propositions

Imposing precision may be difficult and lead to Imposing precision may be difficult and lead to

less than optimal solutionsless than optimal solutions

Fuzzy systems handle imprecise information by Fuzzy systems handle imprecise information by assigning degrees of truthassigning degrees of truth

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Fuzzy Systems (cont’d)Fuzzy Systems (cont’d) FS allow us to express knowledge in vague FS allow us to express knowledge in vague

linguistic termslinguistic terms Flexibility and power of fuzzy systems now well Flexibility and power of fuzzy systems now well

recognisedrecognised Some applications of fuzzy systems:Some applications of fuzzy systems:

Control of manufacturing processesControl of manufacturing processes appliances such as air conditioners and video appliances such as air conditioners and video

camerascameras In combination with other intelligent system In combination with other intelligent system

methodologies to develop hybrid fuzzy-expert, methodologies to develop hybrid fuzzy-expert, neuro-fuzzy, or fuzzy-GA systemsneuro-fuzzy, or fuzzy-GA systems

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Case-based reasoning (CBR)Case-based reasoning (CBR)

CBR systems solve problems by making use of knowledge about CBR systems solve problems by making use of knowledge about similar problems encountered in the pastsimilar problems encountered in the past

The knowledge used in the past is built up as a case-baseThe knowledge used in the past is built up as a case-base

CBR systems search case base for cases with attributes similar to CBR systems search case base for cases with attributes similar to given problemgiven problem

Solution created by synthesizing similar cases, and adjusting to Solution created by synthesizing similar cases, and adjusting to cater for differences between given problem and similar casescater for differences between given problem and similar cases

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Case-based reasoning (cont’d)Case-based reasoning (cont’d)

CBR systems can improve over time by learning from CBR systems can improve over time by learning from mistakes made with past problemsmistakes made with past problems

Application examples:Application examples: Utilisation of shop floor expertise in aircraft repairsLegal Utilisation of shop floor expertise in aircraft repairsLegal

reasoningreasoning Dispute mediationDispute mediation Data miningData mining Fault diagnosisFault diagnosis SchedulingScheduling

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Data miningData mining

The process of exploring and analysing data for The process of exploring and analysing data for discovering new and useful informationdiscovering new and useful information

Huge volumes of mostly point-of-sale (POS) data are Huge volumes of mostly point-of-sale (POS) data are generated or captured electronically every day, eg, generated or captured electronically every day, eg, data generated by bar code scannersdata generated by bar code scanners customer call detail databasescustomer call detail databases web log files in e-commerce etc. web log files in e-commerce etc.

Organizations are ending up with huge amounts of Organizations are ending up with huge amounts of mostly day-to-day transaction datamostly day-to-day transaction data

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Data mining (cont’d)Data mining (cont’d)

It is possible to extract useful information on market It is possible to extract useful information on market and customer behaviour by “mine”-ing the dataand customer behaviour by “mine”-ing the data

Such information may Such information may indicate important underlying trends and associations in indicate important underlying trends and associations in

market behaviour, andmarket behaviour, and help gain competitive advantage by improving marketing help gain competitive advantage by improving marketing

effectivenesseffectiveness Techniques such as artificial neural networks and Techniques such as artificial neural networks and

decision trees have made it possible to perform data decision trees have made it possible to perform data mining involving large volumes of data.mining involving large volumes of data.

Growing interest in applying data mining in areas such Growing interest in applying data mining in areas such direct target marketing campaigns, fraud detection, and direct target marketing campaigns, fraud detection, and development of models to aid in financial predictionsdevelopment of models to aid in financial predictions

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Intelligent software agentsIntelligent software agents (ISA)(ISA)

ISAs are computer programs that provide active ISAs are computer programs that provide active assistance to information system usersassistance to information system users

Help users cope with information overloadHelp users cope with information overload Act in many ways like a personal assistant to the user Act in many ways like a personal assistant to the user

by attempting to adapt to the specific needs of the userby attempting to adapt to the specific needs of the user Capable of learning from the user as well as other Capable of learning from the user as well as other

intelligent software agentsintelligent software agents Application examples:Application examples:

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

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Language Technology (LT)Language Technology (LT)

““Application of knowledge about human language in computer-based Application of knowledge about human language in computer-based solutions” (Dale 2004)solutions” (Dale 2004)

Communication between people and computers is an important aspect of Communication between people and computers is an important aspect of any intelligent information systemany intelligent information system

Applications of LT:Applications of LT: Natural Language Processing (NLP) Natural Language Processing (NLP) Speech recognitionSpeech recognition Optical character recognition (OCR)Optical character recognition (OCR) Handwriting recognitionHandwriting recognition Machine translationMachine translation Text summarisationText summarisation Speech synthesisSpeech synthesis

A LT-based system can be the front-end of information systems A LT-based system can be the front-end of information systems themselves based on other intelligence toolsthemselves based on other intelligence tools