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BUSS 951 Critical Issues in Information Systems Lecture 9 Systems for Organisations 3: Knowledge Technologies Clarke, R. J (2001) L951-09: 1 Notices (1) General Assignment 2 is due today, and will be available at the beginning of week 11 Assignment 3 will be put up on the web during the first week of the holidays- download it at your leisure Please check that your Assignment 1 mark is correctly recorded, current marks are available on the departmental notice board and pick up assignments if you have not yet done so BUSS951 is supported by a website (available from Tomorrow), where you can find out the latest Notices get Lecture Notes, Tutorial Sheets, Assignments etc www.uow.edu.au/~rclarke/buss951/buss951.htm Clarke, R. J (2001) L951-09: 2 Notices (2) Readings for this week Last week we postponed seminar discussion of the allocated readings in order to discuss arguments for Assignment 2, this week we will discuss these readings: 1. Yu, E. (1998) “Why agent-oriented requirements engineering” Reading 6 2. Yu, E. S. K and J. Mylopoulos (1994) “From ER’ to A-R’- Modelling Strategic Actor Relationships for Business Process Reengineering” Reading 7 Clarke, R. J (2001) L951-09: 3 Agenda (1) Discuss some problems with traditional systems analysis views of work in offices Promote a view which looks at office work in terms of action and human communication (similar to a Systems Auditors View of an IS) Introduce the ideas behind Action Workflow (one type of LAP approach) Clarke, R. J (2001) L951-09: 4 Decision Support Systems Clarke, R. J (2001) L951-09: 5 Decision Support Systems (1) information producing system aimed at a particular problem: that a manager must solve decisions that a manager must make Programmed Decisions repetitive and routine definite procedure for handling them Nonprogrammed Decisions novel and unstructured problem hasn’t arisen before Programmed Decisions Nonprogrammed Decisions Continuum Clarke, R. J (2001) L951-09: 6 Decision Support Systems (2) Phases of Decision Making Intelligence Activity searching environment for problems Design Activity inventing, developing, analysing possible courses of action Choice Activity selecting a course of action Review Activity assessing past choices Clarke, R. J (2001) L951-09: 7 Developing DSS using Simons phases can build systems which perform this work use a special form of systems development called rapid prototyping (see appropriate lecture) Clarke, R. J (2001) L951-09: 8 DSS Capabilities (1) May use one or more techniques depending on the problem: Mathematical Modelling Simulation Graphics Visualisation (Virtual Reality) Clarke, R. J (2001) L951-09: 9 DSS Capabilities (2) Mathematical Modelling Static Model (doesn’t include time) Dynamic Model (includes time) Deterministic Model (avoids probability) Probabilistic Models (includes probability) Optimising Model (selects best solution) Suboptimising Model (‘satisficing’ solution) Clarke, R. J (2001) L951-09: 10 DSS Capabilities (3) Steps towards Simulation: scenario & scenario data elements managers input is referred to decision variables (With McDonalds Example: people, number of queues, expected load over a day) Clarke, R. J (2001) L951-09: 11 DSS Model Individual Problem Solvers Other Group Members Report writing software Math Models GDSS Software Database Environment Environment Data Info Coms Clarke, R. J (2001) L951-09: 12 DSS/ES Decision Support Systems applications are beginning use a technology called Expert Systems an Expert System (ES) comprises a collection of facts and a set of rules which are processed by an inference engine we will discuss these in greater depth latter in the lecture Clarke, R. J (2001) L951-09: 13 Executive Information Systems Clarke, R. J (2001) L951-09: 14 Executive IS (1) Executive Information Systems (EIS) are designed specifically for managers on the strategic planning level executive activity is not very structureddifficulties in establishing an understanding of executive problem solving Warning: problems with acronyms- the term EIS has a variety of definitions it is also been defined as Enterprise Information Systems Executive Information Systems Clarke, R. J (2001) L951-09: 15 Executive IS (2) three ways of achieving EIS: develop custom software application end-user development software (spreadsheets, database, graphics) install special EIS software EIS characteristic- drill down executive begins with an overview gradually retrieve more information Clarke, R. J (2001) L951-09: 16 Executive IS EIS Characteristics- Drill down executive begins with an overview and then gradually retrieves more specific information often by accessing lower level systems (transaction processing or operational systems) Clarke, R. J (2001) L951-09: 17 Executive IS EIS Characteristics- Slice and Dice taking a large database progressively searching through the data using various combinations of variables in order to understand a complex problem which is resolvable Clarke, R. J (2001) L951-09: 18 Executive IS (3) sometimes a distinction is made between EIS and ESS Executive Support Systems (ESS) support information needs also communications & analysis needs does this by providing intelligence ie. by understanding how information affects operations. Does this distinction make any sense? Clarke, R. J (2001) L951-09: 19 EIS Model Executive Workstation Executive database Personal Computer To other Workstations Corp. Database Electronic Databases Info Requests Info Display To other Workstations Make Corp. Info. available External data & info. Current news, explanations Software Library Corporate Mainframe Clarke, R. J (2001) L951-09: 20 Concept of Organisational Information Subsystems (1) subsets of MIS tailored to meet users needs in functional areas examples: marketing information systems manufacturing information systems financial information systems human resources information systems Clarke, R. J (2001) L951-09: 21 Concept of Organisational Information Subsystems (2) Remember: Nothing physically separates these systems- rather these systems are logical distinct databases used by one organisational subsystem can be used by others programs may also be shared Clarke, R. J (2001) L951-09: 22 Concept of Organisational Information Subsystems (3) these systems are not an alternative to a firm-wide MIS need these subsystems to have a complete organisation wide MIS development strategy: MIS then implement organisational information subsystems Clarke, R. J (2001) L951-09: 23 Organisational Information Systems EIS Marketing Financial Human Resource Manufacturing Information Systems Clarke, R. J (2001) L951-09: 24 Knowledge Systems Artificial Intelligence & Expert Systems Clarke, R. J (2001) L951-09: 25 Knowledge Systems AI vs ‘Non-Intelligent’ programs Artificial Intelligence (AI) has emerged as one of the most significance technologies of this century subfield of computing science that is concerned with symbolic reasoning and problem solving, by manipulation of knowledge rather than mere data classical ‘non-intelligent’ computer programs: are rigid, structured procedures that deal with specific problems programs may be flexible and may be capable of dealing with complex situations everything non-intelligent programs do is predictable or preordained Clarke, R. J (2001) L951-09: 26 Knowledge Systems Intelligent Programs Intelligent Programs can on occasion exhibit behaviours that were not programmed consists of a complex set of rules on how to process data as well as having information stored in a database thee programs are generally goal directedthey are designed to behave in order to reach their goals rather than being told how to achieve them Clarke, R. J (2001) L951-09: 27 Knowledge Systems Heuritsics- Rules of Thumb A key characteristic of AI programs is heuristics or rules of thumb which guide the execution of the program: Conventional Programs Often primarily numeric Algorithmic- solution steps explicit Integrated information and control Difficult modification Correct answer required Best possible solution usually sought AI Programs Primarily symbolic processes Heuristic search- solution steps implicit Control structure usually separate from knowledge domain Usually easy to modify, update and enlarge Some incorrect answers are often tolerable Satisfactory answers usually acceptable Clarke, R. J (2001) L951-09: 28 Knowledge Systems AI: A Broad Spectrum of Technologies Expert systems Natural language Speech processing Vision Robotics Cognitive modelling Knowledge representation and utilization Problem solving and inference Learning- knowledge acquisition Special purpose computer hardware Clarke, R. J (2001) L951-09: 29 Knowledge Systems Expert Systems “An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution. The knowledge necessary to perform at such a a level, plus the inference procedures used, can be thought of a s a model of the expertise of the practitioners of the field” “the knowledge of an expert consists of facts and heuristics. The ‘facts’ constitute a body of information that is widely shared, publicly available, and generally agreed upon by experts in a field. The performance level of an expert system is primarily a function of the size and quality of the knowledge base that it processes” (Feigenbaum, E. A. & P. McCorduck 1983) Clarke, R. J (2001) L951-09: 30 Knowledge Systems Expert Systems Applications Typical expert systems applications (O’Brien, 355): Decision Management (recommendations) Diagnostic and troubleshooting Maintainence and Scheduling (prioritizing, scheduling) Intelligent text and documentation (legal documents) Design/configuration Selection/classification (suspect id) process monitoring/control (chemical testing) Clarke, R. J (2001) L951-09: 31 Knowledge Systems Expert Systems-Architecture architecturally expert systems are considered as rule-based systems they provide a separation between the knowledge base, and the inference engine, and may contain a built-in explanation facility (reasoning) and a dialogue component(based on computation linguistics) Clarke, R. J (2001) L951-09: 32 Knowledge Systems ES Application Areas & Problem Classes Expert systems application areas are often divided into particular domains analysis problems which include: debugging, diagnosis, and interpretation synthesis problems which include: Classes Diagnosis Design Planning Simulation Approach Pattern recognition Object Generation Action Generation Modelling Configuration Planning, and scheduling Clarke, R. J (2001) L951-09: 33 ES System Prescriptions developed by Stefik et al (1984), this table consists of the requirements and possible development prescriptions that might be used when developing expert systems three major development options include: when there is unreliable data or knowledge when the data are time varying, and when the problem to be solved involves a big solution space Clarke, R. J (2001) L951-09: 34 Knowledge Systems Expert Systems Development interestingly although we might think of expert systems as somehow privilege and special, in fact building them is similar to other systems notice the prototyping cycle in the lower half of the diagram this is what is referred to as Rapid Prototyping and is found in conventional systems development as well here it is rather well suited to the process of rule creation and improvement ( = knowledge acquisition) Clarke, R. J (2001) L951-09: 35 Expert System Classification Knowledge vs Technological Complexity Broad scope, great depth, information ambiguity, high database mgmt, integrity Clarke, R. J (2001) L951-09: 36 Knowledge Systems Types of Expert Systems 1: Low Tech 1. 2. Personal productivity systemssimplest system, eg. Personal budgeting systems running on PCs, built using an expert systems shell Power Decision systemknowledge intensive incorporating skills of highly skilled decision makers, goal top improve decision making of a group eg. REVA- repairs and faults with electric pumps centrifuges etc Clarke, R. J (2001) L951-09: 37 Knowledge Systems Types of Expert Systems 2: High Tech 3. 4. Integrated production systems- limited amounts of domain complexity but involve advanced technology. Tend to target organisational productivityimproving throughput, reducing headcounts and lowering costs, eg. Telex Router receives all telexes coming into a banks headquarters, reformats them and routes them to appropriate employees Strategic Impact Systems- are both broad and deep in their domains. The decision process is long and intricate, and often requires testing of numerous hypotheses. Example: Lincoln National Life Underwriting System which knows about medical, financial and insurance fields Clarke, R. J (2001) L951-09: 38 Knowledge Systems Benefits of Expert Systems 1. Improve personal decision making 2. Improve group decision making and enhance the quality of product service 3. Improve organisational throughput and costs 4. Create market barriers and improve organisational decision making and productivity Clarke, R. J (2001) L951-09: 39 Knowledge Systems Benefits of Expert Systems 1. 2. 3. 4. Personal Productivity Systems: end user- supported by information systems Power Decision Systems: existing business unit- close to experts Integrated Production Systems: Data processing/MIS departments- driven by technical needs Strategic Impact Systems: new system business unit- full time, resource hungry, new business generating Clarke, R. J (2001) L951-09: 40 Integrating Expert Systems Bowerman & Glover (1988) identified five different types of integration between IS and ES based on production environment requirements: Standalone eg. Personal Consultant Plus Business eg. ACORN & Lotus 1-2-3 Scientific eg. Rulemaster & Informix Process Control: eg. PICON & GKS & TDC-3000 Decision Support: eg. TIMM & Comshare & Nichols & Versatec Clarke, R. J (2001) L951-09: 41 Integrating Expert Systems a major area of research activity was how to construct standardised interfaces between numerous expert systems in the diagram on left- a hypothetical process control function- the inputs constitute data collected from sensors and condensed by pre-processing computer outputs consist of conventional computer for controlling processes Clarke, R. J (2001) L951-09: 42 Conclusions Some Critical Issues we have concentrated on expert systems as new of the most useful ‘knowledge’ technologies in information systems yet are expert systems knowledgeable- would we be thinking of these technologies as intelligent if the metaphor we used was not anthropomorphic (human-like or of human form) if we though of these technologies as simply reproducing patterns where the patterns were in form of sets of rules- would we kid ourselves that there were intelligent this is probably simply the same kind of discourse (way of speaking & way of thinking) as is illustrated in the cybernetic organisation metaphor we talked about earlier Clarke, R. J (2001) L951-09: 43 Conclusions Some Critical Issues the facts and heuristics of expert systems for example constitute a particular ontological and epistemological position on the world this definition of expertise ignores for example that with humans these facts are usually acquired through the senses and with the aid of a physical body rather than simply being a consequence of which rational cognition this is not to deny the usefulness of these systems in the niche markets which they address- for many kind of computation these and related technologies are simply the only sensible way of solving certain class of computational problem Clarke, R. J (2001) L951-09: 44 Conclusions Some Critical Issues it is to remind us that equating this kind of programmatic execution to intelligence is at best a trope … a word or expression used in a figurative sense Clarke, R. J (2001) L951-09: 45 References Gottinger, H. W. and H. P. Weinmann (1990) Artificial Intelligence, A Tool for Industry and Management Chapters 1 and 3, pp. 9-12 & pp. 22-31 Meyer, M. H. & K. F. Curley (1991) Putting Expert Systems Technology to Work Sloan Management Review 32 (2), pp.31 Bowerman, R. G. and D. E. Glover (1988) Putting Expert Systems into Practice Van Nostrand Reinhold Chapter 8, pp. 287-297 & pp. 307-317 Murray, J. T. & M. J. Murray (1988) Expert Systems in Data Processing: A Professional Guide, Pitman Publishing Co., Chapter 1 Clarke, R. J (2001) L951-09: 46 Feigenbaum, E. A. & P. McCorduck (1983) Fifth generation Reading, Mass.: Addison-Wesley Stefik, M et al (1984) “The organization of expert systems: a perspective tutorial” in Hayes-Roth, F. et al (1984) Building expert systems Addison-Wesley: Redaing, Mass. Clarke, R. J (2001) L951-09: 47