A SNAPSHOT OF KDS 
A KNOWLEDGE DF_,LIVERY SYSTEM 
James A. Moore end William C. Mann 
USCIlnformaUon Sciences Institute 
Marina del Ray, CA 
June, 1979 
SUMMARY 
KDS Is a computer program which creates 
multl-par~raph, Natural Language text from a computer 
representation of knowledge to be delivered. We have 
addressed a number of Issues not previously encountered In 
the generation of Natural Language st the multi-sentence 
level, vlz: ordering among sentences and the scope of each, 
quality comparisons between alternative 8~regations of 
sub-sententJal units, the coordination of communication 
with non-linguistic activities by • gcel-pursuin~ planner, 
end the use of dynamic models of speaker and hearer to shape 
the text to the task at hand. 
STATEMENT OF THE PROBLEM 
The task of KDS is to generate English text under the 
following constraints: 
1. The source of information Is a semantic net, having no 
a priori structuring to facilitate the outputtlng task. 
This represents the most elaborate performance of KDS to 
date. 
SYSTEM DESIGN 
The KDS organization reflects our novel paradigm: 
FRAGMENT- AND-COMPOSE. KDS decomposes the original 
network into fragments then orders and 8~regatas these 
according to the dictates of the text-producing task, not 
according to the needs for which the internal representation 
was originally conceived. KDS has shown the feasibility of 
this approach. 
The KDS organization Is a simple pipeline: FRAGMENT, 
PLAN, FILTER, HILL-CLIMB, and OUTPUT. 
FRAGMENT transforms the selected portion of the 
semantic net into an unordered set of propositions which 
correspond, roughly, to minimal sentences. 
2. The text is produced to satisfy an explicit goal held by 
the text generating system, which describes a desired 
cognitive state of the reader. 
3. To achieve the desired state of the reader requires more 
than a single sentence. 
R ESULTS 
This is not the forum for a extensive analysis of our 
results; for details, see Mann and Moore \[ 1979\]. However, to 
communicate the flavor of what ~ve have 
accomplished--from the motivating goal: 
(WANTS SPEAKER 
(KNOWS HEARER 
F ldlE-ALARM-SCENE)) 
and about two pages of formal propositions describing the 
"Fire-alarm scene', KDS generated the following: 
W Aeneeor there is a \]'ire, the ~arm s~jttm is started 
whic~ Jounds the a/arm bell and starts the timer. W~n it b 
ninety seconds a\[tor t~t timer L~ started, unless tAe o/arm 
J~$tem is cancelled it co~IS Wells Farfo. Wht~ Wells Far~ 
is called, ff CO~Is tat Fire Dept.. 
W t)en ~ou kear tkt o/arm bill or ~ou smell smoke, stop 
¢utr~tMng. determine mheth,r there Is a firs and decide to 
permit the alarm J~stem or to cancel it. When ),ou dttermine 
mketker there Is a \[ire. l\[ t~ere iS, permit t~t alarm S~sttm; 
otherwise cancel it..W~en ~ou permit the alarm syst~, c~! 
the Fire Dept. if possible and \[oilo~ tkt w~uatlon 
procedure. When ~ carroll tke elate s~)sttet, l\[ it iS mote 
t~an n~ner~ seconds since the timer is started, tke alarm 
s.Tsttm e~ls Wells Fargo: ockormlse continue tmrrytldng. 
PLAN uses goal-sensitive rules to impose an ordering on 
this set of fragments. A typical planning rule is: 
"When conveying a scene in which the hearer is to 
identify himself with one of the actors, express ell 
propositions involving that actor AFTER those which 
do not, and separate these two partitions by a 
paragraph break'. 
FILTER, deletes from the set, ell propositions currently 
represented as known by the hearer. 
HILL-CLIMB coordinates two sub-activities: 
AGGREGATOR applies rules to combine two or three 
fragments into a single one. A typical aggregation rule is: 
"The two fragments 'x does A' and 'x does B' can be 
combin~! into a single fragment: 'x does A and B'". 
PREFERENCER evaluates each proposed new fragment, 
producing a numerical measure of its "goodness". A typical 
preference rule is: 
"When instructing the hearer, lncremm the 
accumulating measure by 10 for each occurrence of 
the symbol 'YOU'". 
HILL-CLIMB uses AGGREGATOR to generate new candidate 
sets of fregments, and PREFERENCER, to determine which 
new set presents the best one-step improvement over the 
current set. 
The objective function of HILL-CLIMB has been 
enlarged to also take into ecceunt the COST OF FOREGONE 
OPPORTUNITIES. This has drastically improved the initial 
performance, since the topology abounds wtth local maxima. 
KDS has used, at one time or another, on the order of 10 
planning rules, 30 aggregation rules and 7 preference rules. 
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The aggregation and preference rules are directly 
analogoua to the capabilities of linguistic eempotence and 
performance, respectively. 
OUTPUT lsa simple (two pages of LISP) text generator 
driven by a context free grammar. 
ACKNOWLEDGMENTS 
The work reported here was supported by NSF Grant 
MCS- 76-07332. 
REFERENCES 
Levin, J. A., and Goldman, N. M., Process models of reference 
in context, I$I/RR-78o72, Information Sciences 
Institute, Marina del Re),, CA, 1978. 
Levin, J.A., and Moore, J.A., Dialogue Gamest mete- 
communication structures for natural bnguqe 
interaction, Co~ltive Science, 1,4, 1978. 
Mann, W. C., Moore, J. A., and Levin, J. A., A comprehension 
model for human dialogue, in Proo. IJCAI-V, 
Cambridge, MA, 1977. 
Mann, W.C., and Moore, J.A., Computer generation of 
multl-paraq~raph English text, in preparation. 
Moore, J. A., Levin, J. A., and Mann, W. C., A Gool-orianted 
model of human dialogue, AJCL microfiche 67, 1977. 
Moore, J.A., Communication as a problem-solviq activity, 
in preparation. 
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