ON VERBOSITY LEVELS IN COGNITIVE PRO~ SOLVERS 
P. Otrk~ and T. HavrAuek 
Center of Biomathematics, Czechoslovak Academy of Sciences, 
142 20 Prague 4, Vidensk~ 1083, Czechoslovakia 
The aim of the paper is to discuss several issues that 
usually occur when computational linguistics comes into inter- 
actions with so rapidly growing s~eas of artificial intellig- 
ence as it can be seen e.g. in designing expert and consulting 
systems or in the area of automated programming of knowledge- 
-based problem solving systems. We will mention here problems 
of communicating knowledge between machine and researcher (a 
user of a system) which is not an expert in programming tech- 
hiquev Since natural lan~a~e is a "natural" form for express- 
tng knowledge (and most extramathematical knowledge actually 
exists in this form) it could be seemed that natural language 
would be also the best support for communicating knowledge 
through a cognitive process performed on a computer. Is It 
really so? We want to bring arguments for a rather opposite 
claim by pointed ou~ several formats for expressing and commun- 
toatin~ scientific knowledge which differs from usual natural 
language oneso 
What kinds of data structures for expressing knowledge 
and for representing it in a computer memory we need? For 
answering this question we have to disti~p-ish at least two 
roles of a user in the process of conmmnication with machine. 
First of them can be called a speaker t It characterizes the 
situation in which the user loads knowledge into a machine. 
For such a situation there is important that knowledge enter- 
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ing a computer should be precise and exact as much as possib- 
le. So the data structures should enable disambtguation of 
information. Such a requirement leads naturally to hard con- 
straints of input formats. On the other hand, not only for 
the user convenience, it should be desirable to allow highly 
free format of input information to avoid any apriori limitat- 
ions. Thus, the system should be equipped by an effective 
interpreter transforming various kinds of input information 
into internal representation suitable for potential reasoning 
processes. The second role of the user in communication with 
computer can be called a listener~ This situation is rather 
different from the first one. Here the user has to under- 
stand results of computations and (especially in Oonsulting 
systems) also various explanations resulting from reasoning 
processes. These two roles can, of course, infiltrate one into 
another. Analogous roles can be recognized for a machine, too. 
As a teethed for our considerations an AI problem-orient- 
ed designed for an automatic data analysis (called GUHA-80) 
has been chosen. The task of GUHA-80 system is to generate and 
develop interesting views onto given empirical data (recogni- 
ze tnterestin~ logical patterns). These views should represent 
relevant information contained in the data and be useful for 
formation of hypotheses. 
From the point of view of two above mentioned roles of 
user the following types of information in the GUHA-80 system 
could be disttn&-tttshed: 
1. Information coming into system could contain a) data 
(observations on objects), b) supporting knowledge (apriort 
knowledge about the problem in question, answers of questions). 
2. Information coming from the system could contain a) 
trace of computations and reasoning activities, b) results of 
computations, c) explanations (why such and such operations 
have been performed), cf. ~CIN. 
- 143 - 
Let us give a hypothetical example of user's communicat- 
ion with GUHA-80 system: 
GUHA-80: by a sing~le linkage method using euclidian distance 
on the set of objects the following dendro~am 
expressing the similarity between these objects was 
obtained: 
AnX 
CDX 
Is the dendogram in accordance with your knowledge? 
USER: No. 
GUHA-80. May I suggest another pattern using different clust- 
ering techniques? 
USER: Yes. 
GUHA-SO: Do you prefer some of the following techniques: (a 
table of relevant techniques follows) 
It can be easily seen that in such a conversation different 
levels of understanding language are needed. NL level will be 
appropriate mainly for user's answers, simple questions etc. 
But try to express the information oontainqd in the dend~o- 
gram in NL form! Moreover, for oommun~cation process from 
GUHA-80 to the user it will be typical a ~raphio representat- 
ion of information (which in many cases is more transparent 
than ~L one). 
Thus the language understanding take place mainly in the 
case sub lb) i.e. when entering supporting knowledge. But for 
practical reasons it can be performed in a very simple level 
as e.g. in very high level programmAng languages. Examples 
INPUT PORNAT IS ~( )'. VARIABLES ARE 25. CASES ARE I02. 
VARIABLE hL%MES ARE ... , PRINT CORRELATIONSo 
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/ 
In each case, such an understandingmuet lead to 
1. to the elimination of redundant information and in 
such a way to the core of a statement! 
2. to the possibility to work only with minimal cores 
of statements. 
The reason for a second requirement is that a user 
experienced with the system tend~to replace syntactic sugar 
by an appropriate slang to minimize hie effoz~pa/d e.g. to 
punchin~ or typing statements. 
Conclusion, We have distinguished different types of 
communication of scientific knowledge through a mechanized 
cognitive process. It leads first to the claim that not only 
different levels of understanding language but also aifferent 
levels of verbosity e~e needed. Moreoverp in some c~sesthe ; 
use of verbal information can be undesirable or even impossib- 
le. Horeover, in many cases when understanding lance is ~- 
needed it would be enough to understand only a small relevant 
fragment of It. -i 

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