systems

Commercial expert systems How to avoid the pitfalls

by RUSSELL JONES

Abstract: A nzpidly growing number of commercial expert systems are becoming availabLe. In the UK these are mostly PC- based, b~ngi?tg expert systems within the reach of most businesses. It can be di’ficult to sort out the useful from the inadequate. It is also important only to use expert systems where they are really needed.

Keywords: data processing, software techniques, artificial intelligence, expert systems, microcomputers.

Russell Jones ia a technical journalist.

rtificial A intelligence is a complex subject, standing as it does on

the boundaries of psychology, biology and computing. Because of this complexity, most researchers are

quite properly taking a long-term view when assessing the likely fruits of current research projects.

However, software developers rare- ly miss a good money-making oppor- tunity, and have quickly caught on to

the potential of the most commercial- ly attractive aspect of AI - expert systems. Indeed, the one area where

fifth-generation research is already producing software products with real potential for many businesses is ex-

pert systems. The first generation of what might be termed generic - as opposed to industry or application- specific - expert systems is starting to appear on the market.

This is an area where the sagging

UK computer industry is well to the fore. In fact, interest in expert sys- tems is booming in Britain. Although

the USA is ahead in the large systems arena, UK businesses are the biggest market in Europe, largely through the

adoption of smaller micro-based ex- pert systems.

A recent 700-delegate turnout for the fifth British Computer Society Expert System conference is testa- ment to the huge market potential for expert systems - a potential con- firmed by current statistics. Europe’s expert system market is expected to

explode by the end of the decade rising to $3.8B by 1990, from 1984’s

$37M, according to market researcher Frost & Sullivan. The UK will take the major share of this, accounting for

$258.8M in 1990, compared with $l,SM in 1984.

Many software developers are cre-

ating wholly new markets for their products, and, although UK firms are not developing many mammoth sys-

tems such as those to be found in the USA, they are making use of know- ledge-based software on a day-to-day basis. They are also to the fore in finding out where expert systems are most useful.

Personal computers

Where the UK is currently scoring most heavily over the USA in particu- lar is in the adoption of smaller expert

systems running on existing personal computers. Indeed, many UK soft- ware developers are expecting the

success of the PC to provide the impetus for widespread uptake of smaller-scale expert systems. By con- trast, many of the larger systems developed by large US conglomerates are doomed to secrecy due to the competitive edge they are deemed to confer.

One consequence of this is that the recent year has seen a steady trickle of expert systems for the PC. Helix has produced Expert Edge isee page 124),

~0128 no 3 april 1986 001 l-681X~86:030115-05903.00 0 1986 Burtcrworth &Co ll~ublishw~ I.td 115

and other products have appeared from Expertech (page 123) and Intel- ligent Environments (see page 121).

An important aspect of this increase in commercial products is that it has arisen out of an interest in expert system ‘shells’. Many UK software houses see real potential in such shells. In recent moves, which may perhaps best be perceived in short-

term marketing ‘loss-leading’ terms, most of the software houses selling

such shells have released expert sys- tems applicable to certain areas.

Expertech, for example, has come up with an ‘expert advisor’ on the ramifications of the UK’s statutory sick pay legislation; Intelligent Envir- onments has produced a topical guide to the Data Protection Act which becomes UK law in mid-1986; Helix

is actively using a system called CV Filter.

This last system is designed to screen possible job applicants. It gives a good idea of the potential of this first batch of PC-based expert systems, guiding a secretary through the in- formation on a curriculum vitae and its accompanying letter. It is simple in concept, works within a well-docu- mented knowledge domain and ‘ad- vises’ rather than replaces experts. It may seem somewhat trivial, but it does provide a gentle introduction to the topic for possibly sceptical users.

Of the other similar PC-based sys- tems, the Data Protection system in particular has sold well since its launch. It runs on the IBM PC, as do most expert systems and shells deve- loped for microcomputers. It costs under $40, which scotches the myth that all expert systems are prohibitive- ly expensive.

Software developers are hoping that this sort of straightforward ‘informa- tion expert’ will lead to greater accept- ance of expert systems among smaller organizations. There must, of course, remain some doubt as to whether humble PCs are adequate for this type of program. Certainly the reaction from most software developers is that,

although some smaller, slower PCs

may be unsuitable, the newer systems are not. Given the fact that it is possible to generate fairly easily a lOOO-strong rule base on a PC, they are probably correct.

Of course, such fairly simplistic PC-based systems are not the sort of expert system that academics wax lyrical over. However, within conven- tional business environments such systems are already finding a ready and welcome niche - as straightfor-

ward advisors about esoteric areas of knowledge.

Future plans

Expert systems developers expect that, by mid-1986, there will be a pool of such basic systems which are com- mercially successful. This, they say, will allow users to see showcase ex- amples and evaluate better their com- mercial worth. Software developers are looking forward to this autumn’s deregulation of the UK’s banking and stock dealing market as a potential bonanza for expert systems.

DEC in particular is now working on a finance expert system that will allow dealers access to a wide variety of trading information. Others soft- ware developers have admitted similar plans, and there must be an enormous number of systems under covert deve- lopment. The number is unknown because one of the unfortunate prob- lems with such knowledge-based pro- grams is that they are often precluded from discussion because of their in- herent worth to particular client. This sums up the current attractions to many commercial organizations.

There is one dangerous aspect to the sort of success currently being achieved by such fairly simple, com- mercial systems - that expert sys- tems will become ‘flavour of the month’ and will start to be employed in inappropriate ways. If so, guide- lines would be welcome. One UK consultancy has in fact come up with a set of fairly gentle guidelines for the

‘first time’ user of expert systems (see Table 1)

Limitations

There is no doubt that, after some years of false promises, we are now witnessing the first transition of ex- pert programs from the comfortable surroundings of research laboratories to the more demanding world outside. What then of the future of expert

systems? Certainly, to build on the commercial success currently being achieved, it is now important that

researchers attack several major prob- lems that limit progress in knowledge- based systems.

In particular, something must be done to shorten the time needed to interview experts and represent their special knowledge in the form of rules. Most current research in this area appears to be in three primary

areas - to develop ‘intelligent’ edit- ors that assist in entering and modify- ing rules, to develop interfaces that can interview experts and formulate

the rules and to develop learning systems that can induce rules from examples, or by reading textbooks and papers.

Somewhat ironically, to do any- thing at all ambitious along these lines seems to require fundamental ad- vances in our understanding of two core AI topics - the representation and use of knowledge. Although in- ference networks of rules do much to codify the reasoning process that ex- perts use in solving a problem, there is still much that goes on inside experts’ heads that does not appear in the networks - what the experts themselves deem to be ‘intuition’.

This has not stopped the develop- ment of many more expert systems. Application areas vary from further medical diagnosis to chemical and biological synthesis, from mineral and oil exploration to signal interpreta- tion, from military threat interpreta- tion to VLSI design, and from equip- ment fault diagnosis to expert-system

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systems

construction. Already, the expert sys- tem impetus appears to be growing well, and it seems there are few constraints on the ultimate uses of

expert systems. That said, the nature of their design

and construction is changing. The newer expert systems are adding deep knowledge about causality and struc-

ture. These systems promise to be less fragile than current systems and may yield correct answers often enough to be considered for use in autonomous systems, not just as intelligent assist-

ants. Another change is the increasing

trend toward non-rule-based systems.

Such systems, using semantic net- works, frames and other knowledge representation structures, are often better suited for causal modelling. By providing knowledge representations more appropriate to the specific prob- lem, they also tend to simplify the reasoning required. Some new expert systems, using the ‘blackboard’ approach, combine rule-based and non-rule based portions, which co- operate to build solutions in an in-

cremental fashion, with each segment of the program contributing its own particular expertise.

All of this is typical of the explosion of ideas that generally accompanies

Table 1. Guidelines for the use of expert systems*

the development of any new techno- logy. In fact, the stimulus from the

commercial marketplace may well be what was needed to sort out these ideas - the trade fair is undoubtedly a better arbiter of commercial viabi-

lity than learned expert system con- ferences.

The growth of expert systems, coupled with increased computer cap- ability and greater access to com- puters by the public, promises to give virtually everyone access to expertise. That argues above all a bonanza for a good number of commercial software

houses.

One current danger of the upsurge of Is the problem to be solved too trivial? interest in expert systems is that, in assessing the likely usefulness of expert systems, it is easy to get carried away with good ideas and to forget to ask the very important question as to whether an expert system is indeed the correct path to follow. Although each application must have its own criteria, some points are nearly always appropriate.

Is it a recognized problem?

Is any likely payoff quantifiable (and sufficient)?

Is the risk acceptable?

Has the problem already been solved by algon’thmic means?

If the problem is too trivial and the users of the system are people, rather than programs, then non-expert users will learn quite quickly what answers to expect in each situation. Effectively, they will be- come experts themselves. Once they have become experts - or if they already are experts - it will probably be quicker and easier for them not to use the system at all. All that will have been achieved is the education of the users - which can generally be done more cheaply by other means. If it is not, then perhaps it is not really a problem; certainly it will be more of an uphill struggle to persuade people that their system is worth building. The intangible benefits may be marvel- lous, but it does help to convince any sceptics if that can actually be measured. Large projects in unfamiliar territory carry a degree of risk. It may not be possible to perform the task, and certainly the estimation of how long it will take is unlikely to be accurate. If it has, and the results of the algorithmic method are acceptable, then it should probably be persevered with it. The use of heuristic or knowledge-based methods is probably not worth the risk.

contznued

*The ideas contained in this section first appeared in an article by Mike Bell in Interface. the house magazine of Cambridge (:on\ultant\. The author gratefully acknowledges both the company and the author.

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Refuting the question further, it be- comes possible to ask whether the prob- lem is suitable. Certainly, there are already areas which are known to be difficult, and which are normally worth avoiding.

Even if it seems a good idea to use expert systems techniques, the next problem revolves around trying to find an expert willing and able to have’his or her brains picked. Is it certain that suitable expertise is readily available?

Is common sense required?

Is any sort of learning required?

Is vision input required?

Is a partial solution acceptable?

Is it possible to represent the knowledge?

Is there an expert available?

Is the expert suitable?

Is there any suitable literature available?

If it is, there is no way the system can work. As almost any computer user can vouchsafe, computers just do not have common sense.

If it is, then there will be problems, because nobody is anywhere near cracking that yet. True, there are systems which will classify a set of examples, and systems which will produce a set of classification ‘rules’; there are also, of course, systems that will perform cluster analysis. Unless, however, the system can be presented with all possible cases (i.e. the problem could have been solved with table look-up anyway) it is impossible to tell if any rules induced will or will not be sensible.

Although progress is being made in this area, tasks requiring high level processing of image data still tend to be high-risk and expensive.

‘All or nothing’ problems, where the system must deal correctly with every single case, are generally not feasible. Except in very few cases where all the knowledge applicable to a problem is defined and representable, it must be possible to live with the fact that the system will make mistakes. Remember too the absence of ‘common sense’ in all computer systems. It may be possible to design a system to encompass all the world’s knowledge about garden plants; it is, however, unlikely to be able to tell a gardener what to do if he or she accident- ally sprays them with paraffin rather than insecticide.

A useful test here is to ask whether all the concepts involved in using the telephone could be explained to a Martian. Would it all be too long-winded? Would it be possible to convey all the nuances re- quired? Watch out as well for spatial or temporal concepts: these tend to be rela- tively easy for a human to handle, but far more difficult for a machine.

The expert must not just be available for a short period. Eliciting knowledge from the expert may require quite a lot of time - and close cooperation throughout the life of the project is essential.

The expert must not ‘be there’; he or she must also be cooperative, communicative and suitably motivated.

There does not have to be but it helps.

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Given that an expert can be found to Are the human interfaces usable? Nothing is more deterring than a com- help build the system, how it is possible puter system with difficult-to-use inter- to ensure that the final system is accept- faces. This is really just common sense in able? The system may have been built, it building interfaces that are not user- may work perfectly, but things can still hostile. Avoid the use of meaningless go wrong when it comes to using it. command names each with a slightly

different syntax, provide a ‘help’ system, provide the ability to cancel or undo the command last typed, etc. Similarly, avoid the temptation to make systems over- friendly. Don’t force the use of 15 levels of menus before anything can be done, eschew 21 ‘help’ messages for each minor typing error, etc, etc.

Can the system be tested? It may work, but can that be proved? Is testing going to be a problem? Is there sufficient test data? How long will it take to perform each test? Is there actually a ‘right’ result the system can give?

Might there be legal or political problems? When the system gives the wrong results, what will be the consequences? Will there be legal consequences? Will there be political ones if people do not like what the system did?

Will the system be trusted to the right degree? It used to be a standard joke that people believed everything that came out of a computer. Thanks to the sterling efforts of some notable public service organiza- tions, this is no longer the case. So, will too little trust be put in the results? The system will not be trusted if it makes too many mistakes, which can happen when a prototype is put into use too early. Will too much trust be put in the results? The system will inevitablv make some mist- akes. It cannot realistically contain all the knowledge which might be pertinent to the application. Will the results always be taken at face value? In particular, if system has been built which explains its reasoning, giving justification for every result, will users be in an adequate posi- tion to argue?

Can the system be maintained? In some fields new knowledge is invented or discovered every day. Legal know- ledge, depending upon government legis- lation and case law is one example; taxation is another. How long a delay is permissible before updated knowledge is disseminated? Is the dissemination practi- cal?

A couple of final thoughts. It is no good expecting magic, It is still only software. There is no magic in it. There is no escape from all the problems of a normal software system. There will still be the response time, the bugs, the crashes, the hardware faults and all the other minutiae that affect a systems usability and viability to worry about.

These checklists have been rather negative, but checklists often are. They are not designed to put anyone off. The real revolution in artificial intelligence has been in the software community’s perception of it. The things being done now could as well have been done five or ten years ago. The only real limitation is the ability to think up new applications. 0

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