ICT619 Intelligent Systems

1. ICT619 Intelligent Systems Unit Coordinator: Shamim Khan Room 2.065 ECL Building (North Wing) Phone: 9360 2801 Email: s.khan@murdoch.edu.au

2. 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 ICT619 S2-05

3. 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. ICT619 S2-05

4. 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/ICT619will enable access to unit reading materials and links to other resources. ICT619 S2-05

5. Assessment ICT619 S2-05

6. 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 ICT619 S2-05

7. 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 ICT619 S2-05

8. 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 ICT619 S2-05

9. 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 ICT619 S2-05

10. 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 ICT619 S2-05

11. 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 ICT619 S2-05

12. 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 ICT619 S2-05

13. 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 ICT619 S2-05

14. 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) ICT619 S2-05

15. 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 ICT619 S2-05

16. 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: • banking and finance • manufacturing • retail • personnel management • emergency services • law ICT619 S2-05

17. 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 ICT619 S2-05

18. 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. ICT619 S2-05

19. 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 ICT619 S2-05

20. 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 • portfolio optimisation • bankruptcy prediction • financial forecasting • fraud detection • scheduling ICT619 S2-05

21. 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 ICT619 S2-05

22. 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 ICT619 S2-05

23. 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 ICT619 S2-05

24. 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 ICT619 S2-05

25. 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 ICT619 S2-05

26. 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 ICT619 S2-05

27. 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 ICT619 S2-05

28. 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 ICT619 S2-05