|Australian Journal of Educational Technology
1990, 6(2), 153-170.
Expert Assistants, a type of Expert System, can improve productivity where learning time in a job is longer than a day or two. They can profitably be used to provide a new employee with the knowledge accumulated by their predecessor over a period of months or years. Development time using current shells is cost effective. The objectives of this paper are to give the reader (a) an appreciation of the functionality of an expert assistant, (b) a basic understanding of the potential for productivity enhancement from such a system, and (c) an appreciation of the possible extensions to general training systems.
Expert Systems are based largely on symbolic reasoning. This is what differentiates them from traditional computer programs. By symbolic reasoning, we mean the pattern is not hard and fast. The conditions are not based on numbers but on a structured group of explicit symbols; words in our everyday speech. The Expert System has the ability to manipulate these to solve new problems based on previous decisions and their outcomes. As illustrated in Figure 1, Expert Assistants are a subgroup of Expert Systems.
Figure 1. Where the Expert Assistant fits in
Let us look a little more closely at the more familiar type of computer program. It uses sequential programming. The knowledge we have is applied repetitively to our data. Numeric or character data input is manipulated in a manner consistent with what is called algorithmic knowledge. An algorithm is a well defined procedure which will solve a problem in a finite number of steps; we know there will be an outcome.
Data processing systems as they currently exist have used this model since the days of the earliest computers. Our Computer Aided Instruction programs, Computer Managed Learning, Accounting Systems and Database Management systems all use this programming model.
The Expert System is also a computer program, but it uses rules of thumb (also called heuristics) applied to narrow, technical knowledge. Input is usually in words (our symbols above) and inference based on logic theory is applied to the set of rules to process that symbolic input. Information comes from many sources as indicated in Figure 2. A readable description of the basic inference mechanisms of these systems is contained in Bahrami (1988) or Walters and Nielsen (l988).
Expert Systems deal particularly well with areas which are not amenable to algorithmic solutions. Those problems are quite common in industry and government today.
There is a third type of system. We'll call it the Creative and Artistic model; it is non-sequential and adaptive. It is based on common sense knowledge which we all develop over a period of years of acculturation. It is not possible to build an Expert System using this knowledge with either the current computers or with those we can foresee. It is estimated that there are 2(10,000,000) items encoded in our basic common sense. The largest and fastest computers available today would require a lifetime or two to process this much knowledge.
Figure 2. Sources of knowledge for an Expert System (Adapted from Rausch-Hinden, 1988)
Nothing has been built as yet that even remotely approaches the functioning of the cerebellum of even the lower amphibians and reptiles.Those engaged in research on Artificial Intelligence have been particularly interested in making an intelligent pattern recognising machine. Major work has been done in four areas:
This process is illustrated in Figure 3.
Figure 3. Turing test of artificial intelligence
Expert Systems have been able to meet the Turing test in limited areas, but will consistently fail (as will all sections of artificial intelligence) when only common sense is required to arrive at an answer.
Those portions of common sense knowledge which are important to the area of expertise can be explicitly included in the knowledge base, but we would not include items of common sense which did not directly affect the function of the system. For this reason it is of utmost importance that Expert Systems know what they don't know. Most criticisms of expert assistants, expert systems or other areas of artificial intelligence stem from outcomes which seem irrational to the human user.
One of the most famous is the MYCIN expert system, developed to help diagnose diseases of the blood and central nervous system. When presented with a case of a male Caucasian exhibiting high fever and assorted other symptoms, the MYCIN program asked "Is the patient pregnant?" Needless to say, common sense knowledge about the ability of males to be pregnant has since been added to the knowledge base of the MYCIN system.
Figure 4. Expert System Architecture
The knowledge is not always locked away in a person's mind. Rather it may be available in written form, but so voluminous that most employees cannot find the answer they require, even if they know it is contained in the reference manuals. This is a situation where an Expert Assistant has been gainfully employed by organisations.
The Colleague level is not as common as the Assistant. This type of Expert System is used to collate knowledge in a field to leverage productivity for an experienced employee. It can also provide help for less experienced employees such as those who work the night or weekend shifts. Colleague level systems often represent improved Assistant systems. The knowledge base is expanded to provide the system with more expertise.
The Expert level is the least common type of expert system. At this level, knowledge has been gathered from multiple sources and the system can be used in place of an expert. Where there are few experts available or the site is remote or harsh, the Expert level system can be justified. It, again, often represents an improved Colleague level system.
Expert Assistants easily solve four types of problems: Productivity bottlenecks; diagnosis of problems; distribution of policy, knowledge or information; and loss of expertise due to employee turnover or retirement.
Productivity on night or weekend shifts of a process line is often lower and more uneven than in the day time. Due to seniority, those on the night shift are often less experienced. An Expert Assistant can be used to make expertise more widely available. Mt. Isa Mining has a prototype smelting process control system which was developed by Cameron Russel of JK Tech. (Russel, 1990)
Personal Loans Assistant was developed at a New South Wales Bank. These loans account for a high proportion of the Bank's customer borrowings. A few loans officers were identified as experts in the personal loans area, and one served as the Expert for the system. (Zawa, 1988)
American Express Authorizer's Assistant provides an automated authorisation service to merchants who accept American Express charge cards. (Briefly described in Tello, 1987).
Australian Bureau of Statistics Seasonal Trend Analyser was developed as a joint project by the ABS and Fujitsu. It was a staged development with the prototype discussed at the 1988 conference on Application of Expert Systems in Sydney (Cox, et al., 1988). It is now in use in Canberra.
Manuals of policy, procedures or regulations are often daunting to many employees. As the information becomes more complex, there is an increased probability of incorrect information being given to clients because the information is so difficult or time consuming to locate. These documents are easily represented as a knowledge base. Having the knowledge captured in a central location improves the ease and speed with which these manuals can be updated.
Combining a Hypertext interface with an Expert Assistant has special advantages here. Hypertext allows the non-linear connection of text and ideas, a very intuitive and powerful interface technology. Microsoft has published the manuals for their new operating system, OS/2 on a CD ROM disk to facilitate its use. The PC Assistant (Cifuentes, 1989) demonstrates a smaller scale implementation.
Some systems have many manuals and the overlap between them is also significant. An update to one manual may require changes to a dozen others. McDonnell-Douglas would have needed 3.6 tonnes of documents for their newest fighter aircraft. They chose to supply 10 CD-ROM disks with electronic searching and extensive cross references as a better and more usable alternative.
An inhospitable location makes turnover endemic. Some companies have a policy of regularly cycling staff through remote locations. An Expert System can be used to standardise productivity and knowledge transfer at the site. The QUT Staff Orientation prototype (Birtwell, 1990) is designed to help with turnover.
In the past two to three years, we have seen many Expert Systems Shells brought onto the market. An Expert System includes the inference engine to interpret the rules and facts and the user interface, but the knowledge about your problem will be missing. We will discuss the desirable characteristics of a shell later.
Using a good shell is analogous to the difference between a modern car and a Model T, or a modern automatic washing machine compared to the old "copper". The shells are not equally capable, but realistic and well defined goals will allow you to reject those unsuitable and find a shell which can be used for your initial foray.
Even within Sydney, corporate culture differences make expert systems difficult to transfer. Just think of two banks. They both make personal loans, but the criteria used may vary widely. If relevant form numbers are referenced, the problem is compounded.
In professional areas, however, cooperative efforts on the part of national associations are bringing assistant and colleague systems on line. At a recent conference, Hadgraft and Wigan (1990) described work being done to make the publication, Australian Rainfall and Runoff, more helpful to professional civil engineers.
This is particularly true with Expert Assistants. We want to eliminate the bottlenecks in the organisation and improve overall productivity. We can enhance the performance of both the expert and their subordinate by making information and decision rules more widely available.
To determine whether you have a winner, look at the resources available. Are there manuals covering at least a portion of the problem? Is there a willing expert? Is she or he articulate? Will you have management support? Who will be your key team members and what experience do they have?
Set measurable goals. If we haven't set the goals, how do we know when we get there? Measurements can be in terms of decreased demands on the expert's time, increased productivity in the work unit, improved knowledge about the job throughout the work unit, timely availability of induction training for new employees rather than having to wait for the next course, and so on.
Start small. Prove it will work on a part of the problem. This has many benefits. Your team develops experience in the techniques, better focus can be maintained and interface techniques can be tested with the prototype. Additionally, visible results are available sooner and organisational support is enhanced. However, be prepared to throw the prototype away. It is common for the first approach to the problem to be sub- optimal. The techniques learned and problems solved can be put to use in the new version, but don't be afraid to start over. Renovating a poor approach will be more costly and less effective in the long run.
If the Expert acquired their competence by solving many problems in this area and a few people with specialised knowledge spend time helping others solve the problem, it is amenable to this technology.
Are the organisational resources adequate? You will need to develop the system. You will also require access to the expert in reasonable (say 1 hour) time chunks.
Are the ultimate users willing and committed to using the system you develop? They should be consulted early to determine their perceived problem areas and how such a system could help. As the prototype is developed, involving them in the testing and incorporating their constructive criticism will improve their commitment.
Is the knowledge readily available or will it all have to come from the Expert? Here it is important to recognise that the way the job actually gets done and the criteria on which the decisions are actually made may differ from written procedures or from the training the company provides. If the knowledge must all come from the Expert, the demands on their time may obstruct the project and make it infeasible.
Are the facts relatively certain? Are there discrepancies in how they are applied? Can the problem be subdivided into smaller problems to allow you to start small?
Can the Expert solve the problem in 3 minutes to 3 hours? For the Assistant level, the 3 to 10 minute problem is the best. Where the problem requires longer, an Assistant can be useful if a large proportion of the time is spent in gathering data.
Is someone available who has experience with this technology? This may need to be a person from another section of the organisation or an outside consultant. This person can flatten the learning curve for the internal team. For someone with reasonable skills in computing, there are good Shells on the market which can allow the prototype to be developed in less than a month, even without external resources.
Would the lack of an Expert System expose the organisation to significant problems or threats? This is not usually appropriate for a simple Expert Assistant. However, experience gained in the development of the Assistant can lead to better definition of a system to address this problem.
KnowledgePro (R) is now available for Microsoft (R) Windows(TM) version 3 and supports laserdisc video in addition to the capabilities demonstrated at the Conference. KnowledgePro is a product of Knowledge Garden in the US. For further information, or to arrange to see the demonstrations, please contact the author.
PC Assistant (Cifuentes, 1989)
This product was developed for use by persons new to personal computing. It allows them the opportunity to explore concepts in a non-linear manner and guides them to the desired information. It makes use of the graphic capabilities as well as the hypertext features.
QUT Staff Orientation Assistant (Birtwell 1990)
This prototype was developed for the Personnel section at QUT. It is a prototype, but has demonstrated the feasibility of all the features which are planned for the working Staff Orientation Assistant. Three additional campuses were added to the QUT just as the prototype was completed. The final product will include all campuses.
1987 report to the Prime Minister on current/future prospects for applying knowledge-based systems in Australia. Suitable contexts, international experience, current domestic use and research, political and control issues.Bahrami, A. (1988). Designing Artificial Intelligence Based Software. United Kingdom: Sigma Press.
Although this book is essentially a practical programmer s introduction to the Al language LISP, it also gives a readable account of the basic mechanisms of expert systems - backward chaining, forward chaining, heuristic search, uncertain rules etc.Birtwell, C. (1990). Computer Based Staff Orientation System. Report of project work for Graduate Diploma in Business Computing. Brisbane: Queensland University of Technology.
Report and prototype of a system to be considered for Staff orientation training at QUT.Cifuentes, C. (1989). PC Assistant. Report of project work for Bachelor Applied Science Computing.
Report and system for inexperienced users of IBM personal computer systems at QUT.Cox, P., White, T., Sutcliffe, A. and Liles, C. A Joint ABS-Fujitsu Prototype Expert System. Proceeding of the Fourth Australian Conference on Applications of Expert Systems.
Preliminary report of development the Seasonal Time Series Analysis system. Useful description of the selection criteria and organisation of the development project.Hadgraft, R. G. and Wigan, M. (1990). Hypertext for Engineering Documents. Paper presented at Seminar on Hypermedia and Multimedia at the World Conference on Computers and Education, Sydney.
Discusses the conversion of Australian Rainfall and Runoff, a major resource document in water engineering to a CD-ROM format, the enhancements to its useability for engineers and the potential for combining self education and operational tools in one location.Harmon, P., Maus, R. and Morrissey, W. (1988). Experts Systems: Tools and Applications. New York: John Wiley.
Another "Handbook of Products" publication; useful classification of software tools under headings of size' capability, usability, cost, etc. Also covers expert systems concepts.Kidd, A. L. (1987). Knowledge Acquisition for Expert Systems: A Practical Handbook. New York: Plenum Press.
Useful book. Anna Hart's chapter on Rule Induction, "Role of Induction in Knowledge Elicitation" provides insight into this method of developing the required policy rules for a system.Koch, J. N. (1989). Toward the development of artificial stupidity. Journal of Irreproducible Results.
Tongue in cheek treatment of some of the problems with artificial intelligence.Macquarie University. (1982). The Concise Macquarie Dictionary. Lane Cove, NSW: Doubleday.
Nisenfeld, A. E. (Ed) (1989). Artificial Intelligence Handbook. New York Instrument Society of America.
Two volumes, Principles and Applications. Engineering and process control bias. The chapter, "Get Ready, Set ..." by James Davis is a useful guide to selecting and managing an Expert System project.Oxman, S. W. (1989). The quiet revolution in the expert system area. The Journal of Knowledge Engineering, 2(2), 57-62.
Recommended for its more recent look at Expert System shells and the factors which should be considered when appraising the ones on offer.Prerau. D.S. (1990). Developing and Managing Expert Systems: Proven techniques for business and industry. Reading, MA: Addison-Wesley Publishing.
Includes many examples from the development of a large industrial expert system for GTE Laboratories. Provides step by-step guidance for the inexperienced as weld as some practical advice. Also useful for those who have previous experience with the technology.Rausch-Hindin, W. B. (1988). A Guide to Commercial Artificial Intelligence. New York: Prentice-Hall.
Recommended for its extensive coverage of the range of commercially available expert systems shells (as of 1988) and for the analysis of the factors involved in selecting a software tool for expert systems development.Russel, C. (1990). Development of a Real Time Advisory Process for Control of Flotation Process. Presentation to Expert System Association of Queensland, February.
Russel described the development of the prototype for Mt Isa Mines.Silverman, B.G. (1987). Expert Systems in Business. New York: Addison-Wesley.
A collection of readings with contributors from business. Includes examples of expert systems in resource scheduling, procurement, retrieval, project support, worksheet enhancement; and a selection of views on expert systems management. The chapter, "A Survey of Issues in Expert Systems for Management," by Robert Blanning is particularly relevant.Tello, E. R. (1987). What progress in being made in AI. Dr. Dobb's Journal, 12(2), February, 108-120.
One of a series by Tello in 1987. The style is readable and the series covers shells, LISP and Prolog as well as extension to the languages.Turing, A. M. (1950). Can a Machine Think? Mind, 59,433 460. Reprinted in E.A. Feigenbaum and J Feldham, Eds. (1963), Computers and Thought. New York: McGraw Hill.
Turing wrote this paper on the possibilities of intelligent machines nearly 10 years before computers were in general use in industry, and nearly 40 years before very large scale integrated circuits gave us the ability to put the power of knowledge based systems into general use.Walters, J. R. and Nielsen, N. R. (1988). Crafting Knowledge-Based Systems: Expert Systems Made Easy/Realistic. New York: Wiley InterScience.
Easy-to-read account of the basic concepts of expert systems, with particular reference to the practical problems of converting human expertise into automated form.Zawa, S. (1988). Expert Systems in the State Bank of NSW. Proceedings of the Fourth Australian Conference on Applications of Expert Systems. Preliminary report of work on the Personal Loans Assistant for the bank.
Remember, the Expert may not necessarily be highly placed but will represent a bottleneck situation.
time it takes to solve the problemAlgorithmic solutions can use the normal programming route. Training is usually better unless turnovers make training uneconomic or appropriately qualified persons are scarce. Problems solved by senses are not appropriate for knowledge based solutions.
quality of solution
cost of solution
feasibility of solution
la. The basic knowledge is available: eg. in procedural manuals, text books, documentation of solved problems. This part is particularly important for the Expert Assistant project as much of the knowledge can be collected from these sources, requiring much less of the Expert's time.
|Author: Sylvia Willie Lectures in Information Systems at the Queensland University of Technology, Brisbane. She is currently Research Manager, Research into improved student access and completion rates for part-time students in Information Technology subjects, a project funded by the Commonwealth Department of Employment, Education and Training. She was responsible for introduction of Computer Managed Learning systems to Information Technology subjects. Before joining QUT, she was a computer systems consultant.
Please cite as: Willie, S. (1990). Expert assistants - productivity to the power of ...! Australian Journal of Educational Technology, 6(2), 153-170. http://www.ascilite.org.au/ajet/ajet6/willie.html