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MSc Computing for Financial Services COM717 Artificial Intelligence - Expert Systems Dr Sandra Moffett
Machine Learning <ul><li>Machine learning is a process of self-improvement where the information processing capability of ...
Machine Learning Techniques <ul><li>ML techniques can be classified as: </li></ul><ul><ul><li>Numerical techniques – these...
What is Artificial Intelligence? <ul><li>Artificial Intelligence (AI) is the field of computer science that studies how ma...
AI FAMILY NATURAL LANGUAGE ROBOTICS PERCEPTIVE SYSTEMS EXPERT SYSTEMS INTELLIGENT MACHINES ARTIFICIAL INTELLIGENCE AI
<ul><ul><li>Many branches of AI, including </li></ul></ul><ul><ul><ul><li>Automatic Programming – once the capabilities of...
AI areas of study
Conventional Programs –v- AI Programs No Conventional Programs AI Programs 1 Handle entities like bits, bytes, numbers and...
<ul><ul><li>Symbolic Processing – knowledge machine needs is based on symbolic structures, on which operations are perform...
<ul><li>Serial Processing – makes </li></ul><ul><li>only one decision at a time  </li></ul><ul><li>(ES, step-by-step proce...
<ul><li>Parallel Processing – makes several concurrent decisions (NN, able to concurrently appraise multiple input).  Conc...
<ul><li>Reasoning is classified under the following classes: </li></ul><ul><ul><li>Formal reasoning – involves the syntact...
<ul><ul><li>Default reasoning – If x cannot be proved to be false then it is assumed to be true </li></ul></ul><ul><ul><li...
<ul><ul><li>Depending on whether (or not) precise and complete knowledge is available reasoning is of two types: </li></ul...
<ul><ul><li>Matching involves the comparison of two or more structures or patterns to discover similarities or differences...
<ul><ul><li>Alan M. Turing created Turing Test to determine if machine is able to simulate human intelligence </li></ul></...
Turing Test (2)
ELIZA <ul><li>A computer program created in mid-1960s that displays some AI.  Eliza can appear to engage in human conversa...
AI Environment <ul><li>AI ‘Shell’:  A collection of software packages and tools used to design, build, implement and maint...
FS Interest in AI <ul><li>In industry such as FS, where product differentiation is hardwon and expertise is actively sough...
Expert Systems <ul><li>An  expert system (ES) , also known as a knowledge based system, is a computer program that contain...
Components of an expert system Knowledge Database This database contains the rules and the cases used in making decisions ...
Expert System Relationships
ES Development <ul><li>Many tool on market today, vary widely in functionality and hardware support requirements </li></ul...
Languages for ES Development <ul><li>LISP – most common general-purpose AI programming language with many features that ea...
Languages for ES Development (2) <ul><li>C and C++ - general purpose language, used extensively for both AI and non-AI app...
ES System Shells and Products <ul><li>ES-specific tool (Shell) contains all essential elements except the domain-specific ...
Popular ES Shells <ul><li>1 st -Class Fusion  – offers a direct, easy to use link to the KB, also offers visual rule tree ...
ES applications <ul><li>Authorizer’s Assistant (AA) –  an American Express ES used for credit authorisation, weeding out b...
<ul><li>Accounting Systems – maintain ledgers, plan budgets and forecasts, analyse expenses, specify costs in terms of vol...
<ul><li>Capital Expenditures Planning – i.e. selection of product line, whether to buy/sell business segment, lease or buy...
<ul><li>Banking – i.e. Mastercard with credit card fraud </li></ul><ul><li>Insurance – underwriting, claims processing, re...
<ul><li>Financial Statement advice for multinational companies – help deal with unique reporting and legal inconsistencies...
<ul><li>Increase productivity and output - better accuracy, quality and reliability </li></ul><ul><li>Function as tutors t...
<ul><li>Fail to adapt to continually changing environment </li></ul><ul><li>Usually confined to very narrow domain, often ...
Summary <ul><li>This lecture covered </li></ul><ul><li>Artificial Intelligence </li></ul><ul><ul><li>Definitions </li></ul...
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FS Lecture 3 Arifici..

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  • This diagram outlines the main streams under the AI umbrella. Lecturer should give a brief explanation of the elements of each.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • Focus of AI in the realm of KM is on expert systems. This slide outlines the three main types of expert systems.
  • This slide outlines why there is an interest in AI from the business community. AI techniques are used mostly to remove boring, repetitive tasks from employees, allowing them more time to focus on productive, knowledge-orientated work.
  • Focus of AI in the realm of KM is on expert systems. This slide outlines the three main types of expert systems.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide gives three examples of expert systems that have been applied in industry. Blue cross blue shield – automated the process of underwriting medical insurance claims. Countrywide Funding Corporation – use expert system to determine potential loan applicants United Nations – use expert system to automatically calculate employee salaries. Lecturer – the above examples can be amended if necessary. Basically, the examples are used to show that while expert systems have a role to play in industry they are often used for repetitive, mundane tasks where the knowledge of experts can be easily recorded to provide information at mass.
  • This slide outlines some of the limitations of expert systems. Each limitation is self-explanatory.
  • Transcript of "FS Lecture 3 Arifici.."

    1. 1. MSc Computing for Financial Services COM717 Artificial Intelligence - Expert Systems Dr Sandra Moffett
    2. 2. Machine Learning <ul><li>Machine learning is a process of self-improvement where the information processing capability of a machine is augmented using the information processing activity of the same. Learning capability must be built into the machine enabling it to direct the knowledge acquisition process and to build internal representations of its own. </li></ul><ul><li>Improvement may be observed in </li></ul><ul><ul><li>Enlargement of knowledge base of the machine by acquiring new information </li></ul></ul><ul><ul><li>Reduction of knowledge-base size by refinement of knowledge and disposal of surplus data. This decreases the minimum space indispensable for solving a problem </li></ul></ul><ul><ul><li>Speed enhancement by including heuristics </li></ul></ul><ul><ul><li>Betterment of solution quality </li></ul></ul><ul><ul><li>Extension of the range of problems solvable by a machine </li></ul></ul>
    3. 3. Machine Learning Techniques <ul><li>ML techniques can be classified as: </li></ul><ul><ul><li>Numerical techniques – these include a kind of algorithm which automatically determines a threshold limit for distinguishing between two objects like apples and grapes on the basis of their size </li></ul></ul><ul><ul><li>Structural techniques – these methods deal with relationships, concepts, sets, trees and graphs or the use of combinatorial optimisation techniques for discovering the classification rules of objects having different features, with little emphasis on numerical magnitudes e.g. classification of different hybrids of colours in terms of their properties (primary, pastel, strong, yellows, etc.) </li></ul></ul>
    4. 4. What is Artificial Intelligence? <ul><li>Artificial Intelligence (AI) is the field of computer science that studies how machines can be made to act intelligently. It is the use of software to simulate the functions of a decision-maker’s mind in carrying out his or her daily job responsibilities, machines must be able to perceive, reason, learn and communication </li></ul><ul><li>Preserve Expertise, make it portable </li></ul><ul><li>Create and/or enhance Knowledge Base – put knowledge and judgement of recognised expertise at fingertips of novices, optimal analysis of data for decision making </li></ul><ul><li>Improve productivity and efficiency, maximisation of revenue and resource </li></ul><ul><li>Mechanism not subject to feelings, fatique, worry, crisis, can eliminate routine/unsatisfying jobs </li></ul>
    5. 5. AI FAMILY NATURAL LANGUAGE ROBOTICS PERCEPTIVE SYSTEMS EXPERT SYSTEMS INTELLIGENT MACHINES ARTIFICIAL INTELLIGENCE AI
    6. 6. <ul><ul><li>Many branches of AI, including </li></ul></ul><ul><ul><ul><li>Automatic Programming – once the capabilities of a computer program are described, the AI system writes the code for it </li></ul></ul></ul><ul><ul><ul><li>Bayesian Networks – the use of probabilistic information by an AI system in making inferences </li></ul></ul></ul><ul><ul><ul><li>Machine Learning – computer programs that learn from experience </li></ul></ul></ul><ul><ul><ul><li>Natural Language Processing – processing and possibly understanding the natural human language </li></ul></ul></ul><ul><ul><ul><li>Speech recognition – converting oral speech to written text </li></ul></ul></ul><ul><ul><ul><li>Visual pattern recognition – responding to visual stimuli in ways similar to the human sense of sight </li></ul></ul></ul>Branches of AI
    7. 7. AI areas of study
    8. 8. Conventional Programs –v- AI Programs No Conventional Programs AI Programs 1 Handle entities like bits, bytes, numbers and functions Operate on knowledge entities such as symbols, concepts, rules and relationships 2 Types of data include numbers and characters Data types are atoms, objects and lists 3 Variables are pre-declared or typed Generally, variables are produced as the solution progresses 4 Based on well-defined procedures divided into sequence of steps Use descriptive languages along with the object manipulation, heuristic, constraint satisfaction and search techniques to solve problems 5 Perform algorithmic computing leading to deterministic values (a unique solution). As precise information representation is done, answers are also precise Carry out inferential computing. Exact information not often required and any satisfactory answer will suffice. Programs may produce uncertain results, often determine and display confidence in the output 6 Involve constant dimensional variables Dimension of the data structures may enlarge or contract with the progress of solution 7 Data and control mechanisms are placed together Knowledge and control mechanism are logically isolated, this has the advantage that knowledge of other domains can easily replace that of any domain 8 Lend themselves to a top-down approach of software development by breaking the project into a number of sub-projects which are further modularised Accommodate a bottom-up development method, because it is only after feeding the expert’s knowledge into the system that the structure of the knowledge is understood
    9. 9. <ul><ul><li>Symbolic Processing – knowledge machine needs is based on symbolic structures, on which operations are performed, typically have ‘top-down’ design </li></ul></ul><ul><ul><ul><li>KB-ES – interacts with users in formulating optimal decisions, continually asks users for facts until it has enough information to make decision, explains logic behind the decision process – continues to learn on experience and able to make better informed decisions </li></ul></ul></ul><ul><ul><li>Subsymbolic Processing – ‘bottom-up’ design approach, ability to process signals as well as symbols, i.e the ability to recognise a face is depends on processing images as multidimensional signals </li></ul></ul><ul><ul><ul><li>NN – obtains positive/negative responses to output, stores and uses for trained decision making </li></ul></ul></ul>Types of Processing
    10. 10. <ul><li>Serial Processing – makes </li></ul><ul><li>only one decision at a time </li></ul><ul><li>(ES, step-by-step processes) </li></ul>Serial Processing
    11. 11. <ul><li>Parallel Processing – makes several concurrent decisions (NN, able to concurrently appraise multiple input). Concurrent events (improve efficiency and provide economical gains) mean three types of events; </li></ul><ul><ul><li>Parallel events – wherein several events take place during the same interval of time </li></ul></ul><ul><ul><li>Simultaneous events – many events occur at the same time </li></ul></ul><ul><ul><li>Pipelined events – refers to the occurrence of events in overlapping timeslots </li></ul></ul>Parallel Processing
    12. 12. <ul><li>Reasoning is classified under the following classes: </li></ul><ul><ul><li>Formal reasoning – involves the syntactic manipulation of data structures to deduce new structures, commonly used in logic and production rule-based systems </li></ul></ul><ul><ul><li>Procedural reasoning – specialised routines or procedures are employed for answering questions and solving problems. Frames and semantic network-based systems use this type of reasoning </li></ul></ul><ul><ul><li>Reasoning by Analogy – new facts are extrapolated from existing knowledge, e.g. If a girl gets good grades in physics and chemistry, she can expect to get good grades in physical chemistry (subject which combines the two) </li></ul></ul><ul><ul><li>Generalisation and Abstraction – involves induction mechanism, e.g. If x is true for every known example, and a large number of such samples can be cited, then x is always true </li></ul></ul>Reasoning
    13. 13. <ul><ul><li>Default reasoning – If x cannot be proved to be false then it is assumed to be true </li></ul></ul><ul><ul><li>Abduction – If x=y and y is true, then x is also true </li></ul></ul><ul><ul><li>Metalevel reasoning – knowledge about the extent of our knowledge and the importance of particular facts is used to solve a problem </li></ul></ul><ul><ul><li>Deductive reasoning – knowledge in the form of predefined rules entered into system and specific actions or conditions derived from these rules (ES) </li></ul></ul><ul><ul><li>Inductive reasoning – knowledge acquired in form of examples, conclusions derived from specific examples (NN) </li></ul></ul>Reasoning (2)
    14. 14. <ul><ul><li>Depending on whether (or not) precise and complete knowledge is available reasoning is of two types: </li></ul></ul><ul><ul><ul><li>Logical reasoning – From the statement ‘rose is a flower’ and ‘all flowers give fragrance’ the computer must be able to conclude that ‘rose gives fragrance’ </li></ul></ul></ul><ul><ul><ul><li>Probabilistic reasoning – available information is incomplete, inexact and inadequate to arrive at desired conclusion (e.g. Predicting strategic planning). Deciding about methods of representing uncertainty, and manipulating knowledge in the midst of uncertainties, is dealt with by probabilistic reasoning – enables AI systems to exploit what is known to make predications or draw inferences </li></ul></ul></ul>Reasoning and Certainty
    15. 15. <ul><ul><li>Matching involves the comparison of two or more structures or patterns to discover similarities or differences </li></ul></ul><ul><ul><li>Matching techniques can be divided into exact, partial, fuzzy and other classes (e.g. RETE algorithm) </li></ul></ul><ul><ul><li>RETE algorithm – performs large number of matches per cycles in production-rule based systems, achieves efficient matching by avoiding repetitive matching on successive cycles (uses matching information and indexing rules to eliminate extensive matching during every cycle) </li></ul></ul>Matching
    16. 16. <ul><ul><li>Alan M. Turing created Turing Test to determine if machine is able to simulate human intelligence </li></ul></ul><ul><ul><li>Involved human interrogator using computer interface (such as e-mail or chatroom) to communicate with another human and machine. If interrogator unable to determine which of two sources is machine , machine said to simulate ‘human intelligence’ (able to trick interrogator into believing machine is human’ </li></ul></ul><ul><ul><li>Interrogator only allowed to observe intellectual behaviour through communication – 5 mins sufficient to make decision </li></ul></ul><ul><ul><li>http://www.fil.ion.ucl.ac.uk/~asaygin/tt/ttest.html </li></ul></ul>Turing Test
    17. 17. Turing Test (2)
    18. 18. ELIZA <ul><li>A computer program created in mid-1960s that displays some AI. Eliza can appear to engage in human conversation without really comprehending what human is saying. Eliza asks human user a question, and uses the answer received to formulate yet another question. The program stores subject information in its databank and uses it to converse about a variety of topics. Eliza also able to detect speech patterns </li></ul><ul><li>Example </li></ul><ul><li>U:PUBLIC.WWWCOM717ELIZA example.docx </li></ul><ul><li>Chat to ELIZA </li></ul><ul><li>http://www-ai.ijs.si/eliza-cgi-bin/eliza_script </li></ul>
    19. 19. AI Environment <ul><li>AI ‘Shell’: A collection of software packages and tools used to design, build, implement and maintain expert system – created by knowledge engineer – can be generic (off-the-shelf) or customised (requiring special preparation) </li></ul><ul><li>Knowledge-based expert system – model of human knowledge </li></ul><ul><li>Rule-based system – AI system based on IF … THEN statements (Bifurcation) </li></ul><ul><ul><li>FORWARD CHAINING: Uses input; searches rules for answer </li></ul></ul><ul><ul><li>BACKWARD CHAINING: Begins with hypothesis, seeks information until hypothesis accepted or rejected </li></ul></ul><ul><li>Interfaces with databases, spreadsheets, programming languages </li></ul>
    20. 20. FS Interest in AI <ul><li>In industry such as FS, where product differentiation is hardwon and expertise is actively sought after, AI constructs can help to offer new and better products at lower people costs. </li></ul><ul><li>AI aids Certified Professional Accountants (CPAs) in performing personal financial services for clients (such as portfolio management and trading activities) </li></ul><ul><li>AI is applicable to financial planning with regard to asset and debt management, insurance policy selection and banking arrangements </li></ul><ul><li>Two key techniques – Expert/Knowledge Systems and Neural Networks </li></ul><ul><li>Other techniques – case-based reasoning, pattern matching, machine learning and fuzzy logic </li></ul>
    21. 21. Expert Systems <ul><li>An expert system (ES) , also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge, and contains the knowledge and analytical skills of one or more human experts </li></ul><ul><li>The expert system interacts with the user by repeatedly asking for relevant data until it is ready to derive a decision or conclusion </li></ul><ul><li>The most common form of expert system is a program made up of a set of rules that analyze information (usually supplied by the user of the system) about a specific class of problems, as well as providing mathematical analysis of the problem(s), and depending on their design, recommend a course of user action in order to implement corrections. It is a system that utilizes what appear to be reasoning capabilities to reach conclusions </li></ul><ul><li>Knowledge-intensive systems (software applications) – capture human expertise in limited domains of knowledge – experts distill their knowledge into a set of laws and enter such ‘know-how’ into the system </li></ul><ul><li>An expert system is a practical solution able to handle complex problems requiring high level of human judgment and expertise, able to communicate with its user through an effective dialogue. Expert systems ask questions, give advice and justify it by providing underlying logic </li></ul>
    22. 22. Components of an expert system Knowledge Database This database contains the rules and the cases used in making decisions Domain Database This database contains the information relevant to the domain (area of interest) Database Management System (DBMS) This system controls input and management of both the knowledge and domain databases Inference Engine This component contains the inference strategies and controls used by experts to manipulate the databases. It receives the request from the user interface and conducts reasoning in the knowledge base, it is the brain of the expert system User Interface The user interface includes the explanatory features, on-line help facilities, debugging tools, modification systems and other tools to help the user use the system effectively Knowledge Acquisition Facility This facility determines how the system acquires knowledge from human experts in the form of rules and facts. It allows for interactive processing between system and user. More advanced technology allow intelligent software to ‘learn’ knowledge from different problem domains – more accurate and reliable than that of human experts
    23. 23. Expert System Relationships
    24. 24. ES Development <ul><li>Many tool on market today, vary widely in functionality and hardware support requirements </li></ul><ul><li>Simple approach - general-purpose AI programming language such as LISP or PROLOG, to design and program ES from scratch </li></ul><ul><li>Complex approach – large hybrid development environment that provides ES shell, user interface builder, other development tools </li></ul>
    25. 25. Languages for ES Development <ul><li>LISP – most common general-purpose AI programming language with many features that ease the task of building symbolic processing systems. Offers the advantage of fast prototyping , at the expense of slower execution. Used extensively for AI rsearch. Also popular for conventional programming with the advent of Common Lisp and Lisp development environments with GUI support. Some important features are: </li></ul><ul><ul><li>Dynamic typing </li></ul></ul><ul><ul><li>Extensibility </li></ul></ul><ul><ul><li>Functions as data </li></ul></ul><ul><ul><li>Garbage collection </li></ul></ul><ul><ul><li>Interactive environment </li></ul></ul><ul><ul><li>Uniform syntax </li></ul></ul><ul><li>http://en.wikipedia.org/wiki/Lisp_(programming_language) </li></ul><ul><li>http://www.lisp.org/alu/res-lisp </li></ul><ul><li>PROLOG – another symbolic general-purpose, high-level programming language, popular for AI programming especially where logic plays a significant role. Difficult to learn </li></ul><ul><ul><ul><li>http://www.swi-prolog.org/ </li></ul></ul></ul>
    26. 26. Languages for ES Development (2) <ul><li>C and C++ - general purpose language, used extensively for both AI and non-AI applications. Gives ES developers flexibility in adapting system to problem domain, very fast – often used when speed is paramount. For instance, back propagation in NN, which requires fast execution, can be handled simply in C/C++ </li></ul><ul><ul><li>Limitations - more difficult to apply as they give little or no guidance on how knowledge should be represented or how mechanisms for accessing KB should be designed </li></ul></ul><ul><li>Java should be used when probability among different platforms is of prime importance. Java also offers automatic garbage collection like LISP. </li></ul><ul><ul><li>Limitations – code written in Java does not execute as fast as C/C++ code </li></ul></ul><ul><ul><li>Java ES Shell (Jess) is a rule engine and scripting environment written in Java. Algorithm called Rete used to match the rules to the facts </li></ul></ul><ul><ul><li>http://herzberg.ca.sandia.gov/jess/ </li></ul></ul>
    27. 27. ES System Shells and Products <ul><li>ES-specific tool (Shell) contains all essential elements except the domain-specific knowledge. Knowledge representation method and inference engine are built-in features </li></ul><ul><li>To use ES shell successfully, the domain characteristics must match those the shell’s internal model expects – user enters appropriate data or parameters and ES provides output to a problem or situation </li></ul><ul><li>Other alternative tools often require little computer experience i.e. MATLAB to interface with spreadsheet packages </li></ul>
    28. 28. Popular ES Shells <ul><li>1 st -Class Fusion – offers a direct, easy to use link to the KB, also offers visual rule tree which graphically shows how rules are related </li></ul><ul><li>Financial Advisor – analyses capital investments in fixed assets, such as equipment and facilities </li></ul><ul><li>Knowledgepro – a high-level language that combines ES functions and hypertext – allows set-up for classic IF-THEN rules, can read SS and DB files </li></ul><ul><ul><li>http://elab.eserver.org/hfl0141.html </li></ul></ul><ul><li>Leonardo – an object-orientated language called COMSTRAT which marketing managers can use to analyse the position of their companies and products relative to their competition </li></ul><ul><li>Personal Consultant (PC) Easy – used to route vehicles in warehouses and manufacturing plants </li></ul>
    29. 29. ES applications <ul><li>Authorizer’s Assistant (AA) – an American Express ES used for credit authorisation, weeding out bad credit risks, and reducing losses </li></ul><ul><li>Watchdog Investment Monitoring System – used by Washington Square Advisors to analyse corporate bonds to enhance clients’ revenue – analysis includes change in financial ratios as an indicator of past performance and predictor of future financial claims </li></ul><ul><li>Plan Power – developed by Applied Expert Systems, analyses company’s financial situation, then matches needs with most appropriate financial products and services. </li></ul><ul><li>A Peat Marwick ES – used to bring more consistency and precision to the auditing of commercial bank loans and provisions for bad debt </li></ul><ul><li>Blue Cross Blue Shield - Automated medical underwriting system </li></ul><ul><li>Countrywide Funding Corp. - Loan underwriting expert system </li></ul><ul><li>United Nations - Employee salary calculations </li></ul>
    30. 30. <ul><li>Accounting Systems – maintain ledgers, plan budgets and forecasts, analyse expenses, specify costs in terms of volume, price, category, etc. </li></ul><ul><li>Tax ES – </li></ul><ul><ul><li>ExpeTAX (Coopers & Lybrand product) used in tax planning and accrual (maze of 3000 rules before outlining client’s best tax options) </li></ul></ul><ul><ul><li>Taxadvisor – used for estate planning </li></ul></ul><ul><ul><li>Corptax examines tax consequences of stock redemptions </li></ul></ul>Accounting Applications
    31. 31. <ul><li>Capital Expenditures Planning – i.e. selection of product line, whether to buy/sell business segment, lease or buy equipment, asset investment </li></ul><ul><ul><li>CashValue – commercially viable capital projects planning ES </li></ul></ul><ul><li>Credit and Loan Applications </li></ul><ul><ul><li>GMAC has invested millions of dollars in Analyst system which evaluates creditworthiness of GMs 10,000 domestic dealerships – deployed in over 300 networked sites, payback over 2m per year </li></ul></ul>Accounting Applications (2)
    32. 32. <ul><li>Banking – i.e. Mastercard with credit card fraud </li></ul><ul><li>Insurance – underwriting, claims processing, reserving </li></ul><ul><li>Portfolio management – security selection, consistent application of constraints, hedge advisor </li></ul><ul><li>Trading Advice – real-time data feeds, trade rule generators </li></ul>Finance Applications
    33. 33. <ul><li>Financial Statement advice for multinational companies – help deal with unique reporting and legal inconsistencies </li></ul><ul><li>24-hour trading programs – ES can work tirelessly 24/7/365 – with traderless ES, smaller companies have better chance of entering global markets </li></ul><ul><li>Hedges – Interest-rate swaps, currency swaps and futures </li></ul><ul><li>Arbitrage – quickly identify and evaluate arbitrate opportunities and trigger transactions </li></ul>ES in Global Financial Market
    34. 34. <ul><li>Increase productivity and output - better accuracy, quality and reliability </li></ul><ul><li>Function as tutors to distill expertise in clearly defined rules </li></ul><ul><li>Capture scare expertise – share knowledge </li></ul><ul><li>Shorter decision time – enhance problem-solving capabilities – reduce errors </li></ul><ul><li>Require fewer personnel </li></ul><ul><li>Retain volatile and portable knowledge </li></ul><ul><li>Improve customer service </li></ul>Benefits of ES
    35. 35. <ul><li>Fail to adapt to continually changing environment </li></ul><ul><li>Usually confined to very narrow domain, often reduced to problems of classification, may have difficulty coping with broad decisions </li></ul><ul><li>Can be large, lengthy, expensive </li></ul><ul><li>Maintaining knowledge base is critical </li></ul><ul><li>Many managers unwilling to trust such systems </li></ul><ul><li>Need for man-machine communication through NLP – user friendly window implementation (voice understanding and answer back in desired language) </li></ul><ul><li>Need for fast development – knowledge acquisition, knowledge representation, testing, system research and innovation in applications are key, guaranteed only by means of leading edge applications </li></ul>Limitations of Expert Systems
    36. 36. Summary <ul><li>This lecture covered </li></ul><ul><li>Artificial Intelligence </li></ul><ul><ul><li>Definitions </li></ul></ul><ul><ul><li>AI Family </li></ul></ul><ul><li>Expert Systems </li></ul><ul><ul><li>Definitions </li></ul></ul><ul><ul><li>Components </li></ul></ul><ul><ul><li>Languages and Shells </li></ul></ul><ul><ul><li>Application areas in FS </li></ul></ul><ul><ul><li>Benefits and Limitations </li></ul></ul>
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