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Computers in Manufacturing Enterprises Expert Systems October 5, 2015 Vandana Srivastava Introduction: Expert Systems (ES) one of the application field of artificial intelligence (AI) most promising for commercial applications computer programs that solve difficult problems that are traditionally solved by human experts cost effective consultant explains reasoning behind any solutions it finds able to learn from experience •http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt Expert Systems? Knowledge-based systems are concerned with applying knowledge to solve problems that usually require human intelligence and/or expertise Expert systems are the knowledge-based systems that consist of computer programs design to represent and apply factual knowledge of specific areas of expertise to solve problems They emphasize domain-specific problem solving strategies and employ self-knowledge to reason about their own interference process and provide explanation or justifications for conclusions reached http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt Expert Systems: Features expert systems are assistants to decision makers and not substitutes for them power of expert systems lies in the specific knowledge about a narrow domain stored in the expert system's knowledge base knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain Example of Decision Problem Mortgage application for a loan for $100,000 to $200,000 IF If there are no previous credits problems, and If month net income is greater than 4x monthly loan payment, and If down payment is 15% of total value of property, and If net income of borrower is > $25,000, and If employment is > 3 years at same company THEN Then accept the applications ELSE Else check other credit rules NASA Example NASA has developed a fleet of intelligent space probes that autonomously explore the solar system To achieve success through years in the harsh conditions of space travel, a craft needs to be able to radically reconfigure its control regime in response to failures and then plan around these failures during it remaining flight NASA expects that the set of potential failure scenarios and possible responses will be much too large to use software that supports preflight enumeration of all contingencies Example: Livingstone2 • reusable artificial intelligence (AI) software system • designed to assist spacecraft, life support systems, chemical plants or other complex systems in operating robustly with minimal human supervision, even in the situation of hardware failures or unexpected events http://ti.arc.nasa.gov/opensource/projects/livingstone2/ Components of Expert Systems Components of Expert Systems A knowledge base consists of all facts, rules, relations etc used by the experts inference systems contains the lines of reasoning followed by the expert to organize and control the steps taken to solve problems in a given domain. http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt A dialog system: user interface which communicates with the knowledgebase through the inference system Expert Systems Representation http://www.igcseict.info/theory/7_2/expert/ Need for Expert Systems • human expertise is a scarce and unique resource • inconsistency in humans in day to day decisions for the same set of data • expert systems are needed because of certain inherent human characteristics that can impair the optimality of decisions http://ie.emu.edu.tr/development/dosyalar/%7Bi6A-ec9-Tbe%7DIEand.ppt Generic Application of Expert Systems Classification - identify an object based on stated characteristics Diagnosis Systems - infer malfunction or disease from observable data Monitoring - compare data from a continually observed system to prescribe behaviour Process Control - control a physical process based on monitoring Design - configure a system according to specifications Scheduling & Planning - develop or modify a plan of action Generation of Options - generate alternative solutions to a problem http://www.umsl.edu/~joshik/msis480/chapt11.htm Roles in Expert System Development Three fundamental roles in building expert systems are: Expert success of the ES depends on the experience and application of knowledge of the people responsible of its development large systems generally require multiple experts Knowledge engineer elicits knowledge from the expert, gradually gaining an understanding of an area of expertise Intelligence, tact, empathy, and proficiency in specific techniques of knowledge acquisition Knowledge-acquisition techniques include conducting interviews with varying degrees of structure, protocol analysis, observation of experts at work, and analysis of cases must also select a tool appropriate for the project and use it to represent the knowledge with the application of the knowledge acquisition facility User http://www.umsl.edu/~joshik/msis480/chapt11.htm How Expert Systems Work ? Knowledge Representation and the Knowledge Base contains both factual and heuristic knowledge Knowledge representation is the method to organize the knowledge of the knowledge base two of the knowledge representation methods are: Frame-based systems: frame specifies the attributes of a complex system Rule-based expert systems: knowledge is represented by production rules (consists of an IF part (a condition or premise) and a THEN part (an action or conclusion)) explanation facility explains how the system arrived at the recommendation http://www.umsl.edu/~joshik/msis480/chapt11.htm How Expert Systems Work Inference Engine combines the facts of a specific case with the knowledge contained in the knowledge base to come up with a recommendation directs the user interface to query the user for any further information it needs facts of the given case are entered into the working memory, which acts as a blackboard, storing all the knowledge about the case inference engine repeatedly applies the rules to the working memory, adding new information (obtained from the rules conclusions) to it, until a goal state is produced or confirmed any one of the several strategies can be used by an inference engine to reach a conclusion http://www.umsl.edu/~joshik/msis480/chapt11.htm How Expert Systems Work ? Backward chaining Forward chaining the inference engine attempts to match the data-driven strategy inferencing process moves from the facts of the case to a goal (conclusion) inference engine attempts to match the condition (IF) part of each rule in the knowledge base with the facts currently available in the working memory If several rules match, a conflict resolution procedure is invoked; for example, the lowest-numbered rule that adds new information to the working memory is fired. The conclusion of the firing rule is added to the working memory. commonly used to solve difficult problems of a design or planning nature, such as, for example, establishing the configuration of a complex product. assumed (hypothesized) conclusion - the goal or subgoal state- with the conclusion (THEN) part of the rule If such a rule is found, its premise becomes the new subgoal. In an ES with few possible goal states, this is a good strategy to pursue. If a hypothesized goal state cannot be supported by the premises, the system will attempt to prove another goal state. Thus, possible conclusions are review until a goal state that can be supported by the premises is encountered. best suited for applications in which the possible conclusions are limited in number and well defined eg: Classification or diagnosis type systems, in which each of several possible conclusions can be checked to see if it is supported by the data http://www.umsl.edu/~joshik/msis480/chapt11.htm ES in Production and Operations Management http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf ES in Production and Operations Management -- 2 Capacity planning • decision area central to the longterm growth of an organization • a capacityplanning ES for the IBM system called a performance expert prototype (PEP) • uses a blackboard as a means of communication between modules, each of which is a specialist in some sub-task Facility Location and Design Facility Layout • decision involves both quantitative factors (eg. labour costs, transportation costs) and qualitative factors (community attitudes, legal problems) • used a group technology-based approach for finding the initial part machine groupings and laying out the machining areas in these groups to minimize materialhandling costs • facilities design expert system (FADES) is used to combine judgemental rules of human experts with quantitative tools • WORKPLACE DESIGNER evaluates layouts and recommends design changes to improve labour productivity http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf Project Management • deals with the scheduling and control of large projects • application of an ES using project management was developed by Levitt and Kunz • they used a prototype which applied AI techniques to modify activity lists and schedules • model was applied to offshore oil platform construction ES in Production and Operations Management -- 3 Aggregate planning • done on a monthly basis over a 6-18month horizon • N. PATRIARCH is a multilevel planning, scheduling and control system that was developed for manufacturing • uses AI knowledge modelling, multilevel networking and advanced economic costing to plan on strategic forecasting and planning Process choice / design • selection of inputs, operations, work flows and methods used to produce goods and services • input selection consist of desired contribution of human skills, raw materials and equipment • first knowledge-based system to deal with process design was GARI • Technostructure of machining (TOM) was used for planning drilled holes Product design • eXpert CONfigur er (XCON) used in the product design specifications of all major Digital Equipment Corporation (DEC) systems • considered factors such as cablelength constraints, sizes of cabinets and power requirements in the configuration of systems http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf Scheduling • Intelligent scheduling and information systems (ISIS) was the first application of ES to job-shop scheduling • opportunistic scheduler (OPIS) , a factory-scheduling system generates constraint-satisfying shop schedules • management analysis resource scheduler (MARS) schedule resources for the space transportation system ES in Production and Operations Management -- 4 Quality management • Statistical process control (SPC) deals with quality control by performing repeated inspection and gather data • data are statistically analysed to suggest modifications in manufacturing process and to optimize the input or anticipate malfunctions Inventory control • US Navy used a retail inventory management (RIM) to assist inventory analysts at the distribution centre and in the processing of critical stock items Maintenance • diesel-electric locomotive troubleshooting aid/computeraided troubleshooting system (DELTA/CATS1) was developed by General Electric Company • IVAN is an ES used for planning raw material safety stock requirements http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf Forecasting • EMEX is a business forecasting system by Expert System International • NOSTRADAMUS is another ES to select an appropriate forecasting technique for a specific forecasting task • when a technique is selected, the system provides suggestions to manager for its correct usage ES in Production and Operations Management -- 5 Distribution Purchasing • different criteria includes transportation costs, fixed costs for opening of warehouses, processing cost to handle different products at the warehouses and scheduling • a strategy to distribute the products to customers as part of its approach • national dispatch router (NDR) assist in truck routing, • responsibility for procuring materials, supplies and services • Lockheed’s expert system ASAP simplifies procurement requests for the end user and ensures http://www.it.iitb.ac.in/~palwencha/ES/J_Papers/ES_APP_OPR.pdf Benefits of Expert Systems no substitute for a knowledge worker's overall performance of the problem-solving task. But these systems can dramatically reduce the amount of work the individual must do to solve a problem, and they do leave people with the creative and innovative aspects of problem solving. Some of the possible organizational benefits of expert systems are: can complete its part of the tasks much faster than a human expert error rate is low, sometimes much lower than the human error rate for the same task make consistent recommendations can capture the scarce expertise of a uniquely qualified expert company can operate an ES in environments hazardous for humans Limitations of Expert Systems Problems with knowledge acquisition Maintaining human expertise in organizations can't easily adapt to new circumstances (e.g. if they are presented with totally unexpected data, they are unable to process it) can be difficult to use (if the non-expert user makes mistakes when using the system, the resulting advice could be very wrong) no 'common sense' (a human user tends to notice obvious errors, whereas a computer wouldn't) Expert System Applications Vandana Srivastava Expert System in Agriculture help the farmers to have a well planning for before start to do anything on their land design an irrigation system for their plantation use select the most suitable Crop variety or market outlet diagnosis or identification of the livestock disorder interpret the set of financial accounts prediction for extreme weather such as thunderstorms and frost Rice crop doctor : developed by National Institute of Agricultural Extension Management diagnose pests and diseases for rice crop and suggest preventive/curative measures AGREX developed by Center for Informatics Research and Advancement, Kerala help the Agricultural field personnel give timely and correct advice to the farmers. extensive use in fertilizer application, crop protection, irrigation scheduling, and diagnosis of diseases in paddy and post harvest technology of fruits and vegetables Farm Advisory System developed by Punjab Agricultural University, Ludhiana to support agribusiness management The conversation between the system and the user is arranged in such a way that the system asks all the questions from user one by one on which it needs to give recommendations http://www.generation5.org/content/2005/Expert_System.asp Expert System in Accounting Auditing used for screening and verifying transactions susceptible to fraud Airline and auto industries also use expert systems to verify the correct value of transactions prior to execution authorization and processing of claims Taxation tax treatment of stocks, investments and dividends guidance for corporate tax accrual and the planning process, value-added tax, tax preparation system, and corporate tax planning for the oil and gas industry for international tax planning and optimization of international corporation tax position TAXMAN, TAXADVISOR , CORPTAX etc Financial Accounting cash flow evaluation, analysis of mergers, acquisitions and other investment decisions determination of financial status by ratios, leases, analysis of financial reports filed with the SEC CASHVALUE , FINANCIAL ADVISOR , PURPOOL etc http://raw.rutgers.edu/miklosvasarhelyi/resume%20articles/chapters%20in%20books/c10.%20expert%20systems%20app%20in% 20act.pdf Expert System in Accounting Personal Financial Planning Management Accounting International Business Machines' FAME FAME is used to assist customers with mainframe capacity decisions and financial planning for the acquisition of mainframe computers by purchase, conditional purchase or lease. Texas Instruments' capital investment system Exxon's revenue recognition, transfer pricing, cost flow and accumulation, and evaluation of credit worthiness http://raw.rutgers.edu/miklosvasarhelyi/resume%20articles/chapters%20in%20books/c10.%20expert%20systems%20app%20in% 20act.pdf Expert Systems in Education computer animation study used as a guide to developer to design 2D and 3D modelling package web based Intelligent Tutoring System (ITS) for teaching new internet technologies to high school teachers Leonardo tutoring system is a useful tool for teaching fault analysis in power system http://www.generation5.org/content/2005/Expert_System.asp Expert Systems in Medicine Generating alerts and reminders attached to a monitor can warn of changes in a patient's condition scan laboratory test results or drug orders and send reminders or warnings through an e-mail system Diagnostic assistance When a patient's case is complex, rare or the person making the diagnosis is simply inexperienced, an expert system can help come up with likely diagnoses based on patient data Therapy critiquing and planning can either look for inconsistencies, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based upon a patient's specific condition and accepted treatment guidelines Image recognition and interpretation Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans CaDet is a computer-based clinical decision support system for Early Cancer Detection DXplain is a clinical decision support systems, developed at the Massachusetts General Hospital http://www.generation5.org/content/2005/Expert_System.asp Expert Systems in Environmental Management Computer-Aided System For Environmental Compliance Auditing Geographic Information Systems (GIS) And Simulation Models For Water Resources Management WaterWare: used a graphical interface to provide interactive decision support information for water resources planners and policy makers as it is accessible in a local area network from a central server, and alternatively through the Internet for remote clients • NO Lab on Wednesday, October 7, 2015 • Minor 2 exam on October 8, 2015 between 5:30 – 6:30 pm at LH 316