Brain Waves 
                           "Viewpoint"
                     November 1986 AI EXPERT 

                  by Alex Jacobson, President, 
                      Inference Corporation
     
     

Expert systems technology enables computer to use human 
expertise, judgments and knowledge to solve business problems in 
an emulation of the way human experts do. There is considerable 
evidence to suggest that this technology, when applied to a well-
focused, sufficiently well-defined domain of interest (e.g., 
authorization of a specific type of credit card, diagnosing of 
faults in a specific piece of equipment, scheduling of a specific 
fleets of vehicles in a specific geographic area or configuring 
of a specific set of machines on a specific factory floor) can 
provide human workers who operate in the targeted domain with 
computer support at levels of performance equal to or better than 
the best human experts in the domain.

The benefit of this capability is to enable computers to 
formulate decisions, to draw conclusions and to propose actions 
in response to the wide variety of unstructured or poorly 
structured problems with which only humans could contend 
heretofore. As a result, this technology makes it possible for 
computers to do the same sorts of tasks that professionals and 
white collar workers presently do in the work force --a necessary 
accomplishment if these workers are to receive automation. The 
significance of these capabilities is more far reaching than the 
technical content, per se, implies. The reason is that expert 
systems technology has matured at a time when the computer 
industry as a whole is moving through a major transition. The 
computer industry has, over the past 30 years, fulfilled much of 
its promise in automating clerical level functions (typing 
drafting, bookkeeping, inventory management, listings, records 
keeping etc.). Business and industry is now focusing attention on 
strategic uses of computers in mission-critical applications. 
These applications, a prime example of which is the American 
Airlines Saber System, can provide a major competitive edge to 
companies able to conceive and to implement them. White collar 
workers implement business strategies, hence it is this segment 
of the work force that will be targeted for computer automation 
as strategic uses of computers are undertaken in business. Expert 
systems technology is a critical component for delivering this 
automation to the professional, technical and administrative 
workers who implement mission-critical applications in business 
and industry. This propitious timing between a new capability 
(i.e., expert systems) and a new requirement (i.e., mission-
critical applications of computers) explains the unusual sense of 
importance that is attributed to expert systems technology 
throughout the world.

Expert systems technology is primarily targeted for use in 
applications software and in software tools that support the 
development and operation of applications and systems software. 
The fundamental difference between an expert system and a 
traditional application program is that such an expert system is 
rich in knowledge about the solution of problems in the 
application domain in which the expert system operates; whereas 
traditional applications are rich in the procedural knowledge 
that instruct the computer how to process data to solve the 
problems in the domain in question. It is this richness in 
knowledge that makes expert systems an enabling technology for 
the use of computers in mission-critical applications. 
Nevertheless, expert systems contain procedural knowledge with 
which to instruct the computer and traditional applications 
contain knowledge about the problem solving. It is the higher 
density and the greater extent of knowledge about problem solving 
that distinguishes expert systems from traditional applications 
programs, and provides them with their unusual functional power.

This fundamental difference leads to all of the basic differences 
in the underlying tools, technology and programming methodologies 
(i.e., knowledge engineering) that set the practice of expert 
systems apart from that of conventional software engineering. In 
order to elicit deep and extensive knowledge about problem 
solving in any but most straightforward industrial task areas, it 
is necessary for the software engineer to develop the expert 
system by means of an iterative or evolutionary development 
process. The reason is that humans cannot divulge the deep and 
subtle levels of knowledge about their problem solving expertise 
that industrial class expert systems require, and are able to use 
effectively in a straightforward debriefing process. Rather, it 
is necessary that the software engineering methodology be capable 
of supporting a development regimen that permits knowledge 
obtained by debriefing to be built into an operating partial 
application so that areas of mission knowledge (i.e., knowledge 
not accessible by straightforward interview) can be identified 
and then added to the partial application to create a more 
complete, yet, perhaps still partial application, which can then 
be used to find still less accessible areas of germane knowledge 
which in turn can be added to the system, and so on. This method 
of evolving the expert system into existence is called "bottom-
up-discovery", and is the distinguishing feature of knowledge 
engineering.

Expert systems tools contain the AI technology required to 
support the process of knowledge engineering for building expert 
systems. They contain the structures required to store a variety 
of different types of knowledge paradigms, an inferencing engine 
that permits this knowledge to be used as the system evolves even 
though the knowledge is added to the system incrementally, 
systems software that allows the knowledge engineering to browse, 
modify, add, delete, understand or otherwise manipulate the 
knowledge in the evolving knowledge base, and tools to assist the 
knowledge engineer to build the expert system including the user 
interface of the resulting expert systems. These tools serve the 
purpose of accelerating the pace with which this new technology 
can be effectively applied.

Expert systems technology is basically a software technology. 
While it has almost exclusively been developed in Lisp, and, in 
recent years, Lisp machines, like all other software technologies 
it is intrinsically portable to other languages and to other 
classes of computers. This is of vital importance. To realize 
their full potential, expert systems must fulfill their role in 
mission-critical applications. This requires that expert systems 
operate effectively and efficiently in conjunction with existing 
computer environments. Hardly any of these existing environments 
support Lisp or incorporate Lisp machines. Since expert systems 
technology is portable, it is clear that it must be ported to 
mainstream computers and connected to mainstream software at the 
levels of traditional languages, systems software and 
applications programs. This requirement cannot be evaded -- nor 
need it be.

Finally, there is the question of culture. Expert systems are 
computer applications that arise from a technology culture that 
is substantively different from the culture that has created 
traditional computer applications. Cross culture communication is 
always difficult. It will be no different in this instance. It 
promises to be one of the more formidable obstacles to 
commercialization of expert systems. Not only does the 
applications programming community face the challenge of 
assimilating this new technology, but business operations 
management as well as end-users also must become both familiar 
and comfortable with expert systems and their implications as 
these systems move into the front office. Management faces the 
challenge of managing business practices in which the underlying 
logic of the practice has been made explicit for the first time 
and for which accountability of performance is documented with 
the scrupulousness of which only computers are capable. End-users 
who have never before used computers must become comfortable with 
these new mechanical assistants -- no simple task given the 
anxiety often incurred by computers in people who have no 
predilection for machines.

Although these obstacles are formidable, they can and will be 
transcended. The benefits of industrial scale expert systems to 
the businesses that employ them promise to be too great for these 
transitional burdens to be anything but passing challenges. 
DP/MIS workers, end-user computing programmers, applications 
software vendors, all will benefit from their efforts to adopt 
this new technology. Therefore, expert systems software will 
inevitably lose its singular name and become "just another" 
commercial software technology as the computer industry continues 
to support the growth of business throughout the world.

commercial software technology as the computer industry continues 
to support the growth of business