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How do Expert Systems and Hypertext Compare? <sum09 1 11>
============================================
An expert system moves through a set of rules in much the same way an
individual selects paths in a decision tree. <link27 -PERM>
For dispensing knowledge, each technology has certain advantages. For
example:
Expert systems offer Hypertext systems offer
==================== =======================
Automatic goal selection Ease of construction
Multiple-variable processing Ease of use
Speed in reaching a decision Ease of modification
Sensitivity analysis (browsing)
Transmission of knowledge
Let's consider the importance of each of these factors.
AUTOMATIC PATH SELECTION An expert system can monitor time, pressure,
temperature, etc. to automatically eliminate certain decision paths
in the search for answers. In contrast, hypertext systems depend upon
operator responses to make selected paths to answers.
CALCULATED DECISIONS Expert systems often include formulas that convert any
number of variable inputs into a single path selection. Instead of
this parallel processing (multiple inputs -- single answer), hypertext
decision systems use a sequence of decision points (serial processing)
in order to convert multiple inputs into a single path.
SPEED An expert system may reach the appropriate decision within a
fraction of a second (i.e., avoiding an aircraft collision). With
hypertext, speed is limited by the ability of the user to complete
multiple sequences of reading, understanding, and selecting choices at
each decision point.
However, hypertext has several significant advantages over expert systems in
dispensing information or finding solutions -- construction speed, time to
learn, knowledge representation, ease of modification, sensitivity analysis, and
transmission of knowledge. Here's how.
CONSTRUCTION SPEED A good 200-rule expert system may take a team of
knowledge engineers two years to build. In contrast, most experienced
computer users <link60 TALENTS NEEDED> can build in one day a good
200-node decision tree leading to relevant advice. Our family of
hypertext products includes a number of utility programs that enable
you to do just that.
TIME TO LEARN It often takes many years for experts in other fields (e.g.,
PROLOG, LISP, SMALLTALK) to efficiently transfer their knowledge into
automatic systems. In contrast, all experts can master the tools of the
hypertext system craft with just a few weeks effort.
KNOWLEDGE REPRESENTATION Two major difficulties exist in building expert
systems. First, how can users acquire expert-level knowledge?
Second, how can this knowledge be represented so that a machine can
generate solutions from it?
In hypertext systems, the knowledge is represented using existing
everyday formats (i.e., text, diagrams, pictures).
MODIFICATION EASE Once completed, expert systems tend to be notoriously
difficult to update or modify (many interactions are often hidden
from users) and then to validate again (who knows when an expert
machine starts or stops producing expertise?). With hypertext
systems, modifications and improvements are as easy as adding
branching comments or footnoting text by using a word processor.
TRANSMISSION OF KNOWLEDGE Expert machines generally do not explain
to users the actual methods that will lead to a particular decision.
With hypertext, users directly participate in each and every decision
that leads to particular expertise. This process of openly
displaying the structure <link37> and uses of knowledge readily
transmits it to users of hypertext systems.
SENSITIVITY ANALYSIS Expert machines usually provide a single answer
supported perhaps by a confidence factor such as 82 percent certainty
(whatever that means). Hypertext systems allow users to rapidly test
alternative paths <link05 BROWSE> to see how sensitive to changes
in the initial assumptions the advice may be.
In my opinion, given the years of AI promises and the relative lack of
success in creation and delivery of workable expert systems, the most
important pocketbooks are rapidly closing against further use of computer
processing of rule-based systems as a method of vending expertise from a
disk -- meaning the Department of Defense seems to be giving up on AI!
Generally, over the last 30 years, technologists haven't made operations
research practical. Consequently, if you can't build machines that are
effective in quantitative reasoning, how can you build machines that are
effective in subjective reasoning. Making subjective decision machines work
is at least several magnitudes more difficult.
Why have such decision machines failed? I think the answer is simple.
Numbers and mathematical processing are a second-order approximation to
reality. Language is the first-order approximation to reality. What does
that mean?
Consider the difference in views of reality contained in these two quotes:
┌───────────────────────────────────────────────────────────┐
│ "If you can't measure it, it doesn't exist" │
│ Fundamental maxim of quantitative thinking │
└───────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────┐
│ "Language determines reality. If you haven't named it, │
│ you can't measure it -- measuring wrong things tends │
│ to produce useless answers." │
│ Fundamental maxim of semantic thinking │
└───────────────────────────────────────────────────────────┘
From my viewpoint, experts neither measure nor calculate. Instead, experts
have superior language that enables them to see, describe, and predict
reality in superior ways.
The foundation of expert machines is calculation while the foundation of
hypertext is classification. That's why I think hypertext is superior in
capturing and dispensing knowledge.