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OCR for page 147
SYNOPSIS OF CAL AUDIENCE DIS=JSSION
Concerns of several varieties were expressed about the knowledge
engineering aspects of e ~ systems. Members of the audience with
direct experience with developing expert systems gave these remarks
special cogency. Expert systems se ~ to work better where good
extensive formulations of the knowledge base already exist. Attempting
co Develop anal Knowledge ease ~~ part or One expert system effort
often fails. The domains of expert systems are often exceedingly
narrow, limited even to the particularity of the individual case.
Given the dependence of the knowledge in expert systems upon the
informants, there exists a rain danger of poor s`ystems if the human
experts are full of erroneous and imperfect knowledge. There is no
easy way to root out such bad knowledge.
On this last point it was noted that the learning apprentice systems
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The human
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alscussea In Mltanellts paper provide some protection.
· . . . .
experts give advice for the systems to construct explanations of the
prior experience, and what the systems learn permanently is only what
these explanations support. Thus the explanations operate as a filter
on incorrect or incomplete knowledge from the human experts.
Concern was expressed about when one could put trust in expert
systems and what was require J to validate them. This was seen ~~ a
major issue, especially as the communication frum the system Acted
towards a clinked "Yes sir, will do". It was pointed out that the
Issue Was exactly the same complexity with humans and with machines, in
terms of the need to accumulate broad-band experience with the system
or human on which to finally build up a sense of trust.
Trust an] validation are related to robustness in the sense used in
Newell's discussion. It was pointed out that one path is to endow such
machines with reasoning for validation at the moment of decision or
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action, when the context is available. This at least provides the right
type of guarantee, namely that the system will consider some relevant
issues before it acts. To make such an approach work requires
providing additional global context to the machines, so the information
is available on which to make appropriate checks.
Finally, there was a discussion to clarify the immediate-knawledge
vs search diagram that Newell used to describe the nature of expert
systems. One can move along an isobar, trading off less
immediate-kna~riedge for more search (moving Can and to the right) or,,
147
OCR for page 148
148
vice-versa, more immediate-knowledge for less search (moving up and to
the left). Or one can move toward systems of increased power (moving
up acrves the isobars) by pumping in sufficient additional knowledge
and/or search in same combination. The actual shape of the
equal-performance isobars depends on the task domain being covered.
They can behave like hyperbolic asymptotes, where further tradeoff is
always possible at the cost of more and more knowledge (say) to reduce
search by less and less. But task drains can also be absolutely
finite, such that systems with zero search are possible, with all
correct response simply known. For these, there comes a point when all
relevant knowledge is available, and no further addition of knowledge
incrust-= performance.
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Representative terms from entire chapter:
work requires
