164 lines
9.1 KiB
Plaintext
164 lines
9.1 KiB
Plaintext
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Brain Waves
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"Viewpoint"
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November 1986 AI EXPERT
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by Alex Jacobson, President,
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Inference Corporation
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Expert systems technology enables computer to use human
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expertise, judgments and knowledge to solve business problems in
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an emulation of the way human experts do. There is considerable
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evidence to suggest that this technology, when applied to a well-
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focused, sufficiently well-defined domain of interest (e.g.,
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authorization of a specific type of credit card, diagnosing of
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faults in a specific piece of equipment, scheduling of a specific
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fleets of vehicles in a specific geographic area or configuring
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of a specific set of machines on a specific factory floor) can
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provide human workers who operate in the targeted domain with
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computer support at levels of performance equal to or better than
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the best human experts in the domain.
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The benefit of this capability is to enable computers to
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formulate decisions, to draw conclusions and to propose actions
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in response to the wide variety of unstructured or poorly
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structured problems with which only humans could contend
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heretofore. As a result, this technology makes it possible for
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computers to do the same sorts of tasks that professionals and
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white collar workers presently do in the work force --a necessary
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accomplishment if these workers are to receive automation. The
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significance of these capabilities is more far reaching than the
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technical content, per se, implies. The reason is that expert
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systems technology has matured at a time when the computer
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industry as a whole is moving through a major transition. The
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computer industry has, over the past 30 years, fulfilled much of
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its promise in automating clerical level functions (typing
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drafting, bookkeeping, inventory management, listings, records
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keeping etc.). Business and industry is now focusing attention on
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strategic uses of computers in mission-critical applications.
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These applications, a prime example of which is the American
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Airlines Saber System, can provide a major competitive edge to
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companies able to conceive and to implement them. White collar
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workers implement business strategies, hence it is this segment
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of the work force that will be targeted for computer automation
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as strategic uses of computers are undertaken in business. Expert
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systems technology is a critical component for delivering this
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automation to the professional, technical and administrative
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workers who implement mission-critical applications in business
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and industry. This propitious timing between a new capability
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(i.e., expert systems) and a new requirement (i.e., mission-
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critical applications of computers) explains the unusual sense of
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importance that is attributed to expert systems technology
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throughout the world.
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Expert systems technology is primarily targeted for use in
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applications software and in software tools that support the
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development and operation of applications and systems software.
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The fundamental difference between an expert system and a
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traditional application program is that such an expert system is
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rich in knowledge about the solution of problems in the
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application domain in which the expert system operates; whereas
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traditional applications are rich in the procedural knowledge
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that instruct the computer how to process data to solve the
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problems in the domain in question. It is this richness in
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knowledge that makes expert systems an enabling technology for
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the use of computers in mission-critical applications.
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Nevertheless, expert systems contain procedural knowledge with
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which to instruct the computer and traditional applications
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contain knowledge about the problem solving. It is the higher
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density and the greater extent of knowledge about problem solving
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that distinguishes expert systems from traditional applications
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programs, and provides them with their unusual functional power.
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This fundamental difference leads to all of the basic differences
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in the underlying tools, technology and programming methodologies
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(i.e., knowledge engineering) that set the practice of expert
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systems apart from that of conventional software engineering. In
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order to elicit deep and extensive knowledge about problem
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solving in any but most straightforward industrial task areas, it
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is necessary for the software engineer to develop the expert
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system by means of an iterative or evolutionary development
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process. The reason is that humans cannot divulge the deep and
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subtle levels of knowledge about their problem solving expertise
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that industrial class expert systems require, and are able to use
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effectively in a straightforward debriefing process. Rather, it
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is necessary that the software engineering methodology be capable
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of supporting a development regimen that permits knowledge
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obtained by debriefing to be built into an operating partial
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application so that areas of mission knowledge (i.e., knowledge
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not accessible by straightforward interview) can be identified
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and then added to the partial application to create a more
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complete, yet, perhaps still partial application, which can then
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be used to find still less accessible areas of germane knowledge
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which in turn can be added to the system, and so on. This method
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of evolving the expert system into existence is called "bottom-
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up-discovery", and is the distinguishing feature of knowledge
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engineering.
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Expert systems tools contain the AI technology required to
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support the process of knowledge engineering for building expert
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systems. They contain the structures required to store a variety
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of different types of knowledge paradigms, an inferencing engine
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that permits this knowledge to be used as the system evolves even
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though the knowledge is added to the system incrementally,
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systems software that allows the knowledge engineering to browse,
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modify, add, delete, understand or otherwise manipulate the
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knowledge in the evolving knowledge base, and tools to assist the
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knowledge engineer to build the expert system including the user
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interface of the resulting expert systems. These tools serve the
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purpose of accelerating the pace with which this new technology
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can be effectively applied.
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Expert systems technology is basically a software technology.
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While it has almost exclusively been developed in Lisp, and, in
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recent years, Lisp machines, like all other software technologies
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it is intrinsically portable to other languages and to other
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classes of computers. This is of vital importance. To realize
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their full potential, expert systems must fulfill their role in
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mission-critical applications. This requires that expert systems
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operate effectively and efficiently in conjunction with existing
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computer environments. Hardly any of these existing environments
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support Lisp or incorporate Lisp machines. Since expert systems
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technology is portable, it is clear that it must be ported to
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mainstream computers and connected to mainstream software at the
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levels of traditional languages, systems software and
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applications programs. This requirement cannot be evaded -- nor
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need it be.
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Finally, there is the question of culture. Expert systems are
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computer applications that arise from a technology culture that
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is substantively different from the culture that has created
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traditional computer applications. Cross culture communication is
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always difficult. It will be no different in this instance. It
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promises to be one of the more formidable obstacles to
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commercialization of expert systems. Not only does the
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applications programming community face the challenge of
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assimilating this new technology, but business operations
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management as well as end-users also must become both familiar
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and comfortable with expert systems and their implications as
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these systems move into the front office. Management faces the
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challenge of managing business practices in which the underlying
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logic of the practice has been made explicit for the first time
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and for which accountability of performance is documented with
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the scrupulousness of which only computers are capable. End-users
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who have never before used computers must become comfortable with
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these new mechanical assistants -- no simple task given the
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anxiety often incurred by computers in people who have no
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predilection for machines.
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Although these obstacles are formidable, they can and will be
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transcended. The benefits of industrial scale expert systems to
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the businesses that employ them promise to be too great for these
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transitional burdens to be anything but passing challenges.
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DP/MIS workers, end-user computing programmers, applications
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software vendors, all will benefit from their efforts to adopt
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this new technology. Therefore, expert systems software will
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inevitably lose its singular name and become "just another"
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commercial software technology as the computer industry continues
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to support the growth of business throughout the world.
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commercial software technology as the computer industry continues
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to support the growth of business |