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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.