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

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

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).

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/ICT619 will enable access to unit reading materials and links to other resources.

Assessment ACTIVITY DUE WEIGHT Workshop participation Continuous 10% Project Week 12 35% Closed-book Exam Nov exams period 55%

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

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

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

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

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

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

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

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

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)

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

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)

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

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

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

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

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

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

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

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

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

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

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

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 Hi, I am Cybelle. What is your name?

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