ICT619 Intelligent Systems

1. ICT619 Intelligent Systems Unit Coordinator: Graham Mann Room 2.061 ECL Building Phone: 9360 7270 Email: g.mann@murdoch.edu.au

2. 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

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 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).

4. 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/ICT619will enable access to unit reading materials and links to other resources.

5. Assessment

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

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

8. 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

9. 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: • Customer service (Customer Relations Modelling) • Scheduling (eg Mine Operations) • Data mining • Financial market prediction • Quality control

10. 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

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 • 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

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 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

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

14. 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)

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 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

16. 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)

17. 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

18. 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

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 is initialised (the chromosomes) • New generations of solutions are produced beginning with the intial population, using specific genetic operations: selection, crossover and mutation

20. 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 • portfolio optimisation • bankruptcy prediction • financial forecasting • design of jet engines • scheduling

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 - using fuzzy logic

22. 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

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 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

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 repairs • Legal reasoning • Dispute mediation • Data mining • Fault diagnosis • Scheduling

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

26. 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

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: • News and Email Collection, Filtering and Management • Online Shopping • Event Notification • Personal scheduling • Online help desks, interactive characters • Rapid Response Implementation

28. Hi, I am Cybelle. What is your name? Language 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

29. 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