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<Paper uid="P79-1012">
  <Title>A SNAPSHOT OF KDS A KNOWLEDGE DF_,LIVERY SYSTEM</Title>
  <Section position="2" start_page="0" end_page="0" type="metho">
    <SectionTitle>
STATEMENT OF THE PROBLEM
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
  </Section>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
SYSTEM DESIGN
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> The KDS organization Is a simple pipeline: FRAGMENT, PLAN, FILTER, HILL-CLIMB, and OUTPUT.</Paragraph>
    <Paragraph position="2"> FRAGMENT transforms the selected portion of the semantic net into an unordered set of propositions which correspond, roughly, to minimal sentences.</Paragraph>
    <Paragraph position="3">  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.</Paragraph>
    <Paragraph position="4"> 3. To achieve the desired state of the reader requires more than a single sentence.</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
R ESULTS
</SectionTitle>
    <Paragraph position="0"> 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:</Paragraph>
  </Section>
  <Section position="5" start_page="0" end_page="51" type="metho">
    <SectionTitle>
(WANTS SPEAKER
(KNOWS HEARER
</SectionTitle>
    <Paragraph position="0"/>
    <Paragraph position="2"> and about two pages of formal propositions describing the &amp;quot;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..</Paragraph>
    <Paragraph position="3"> W t)en ~ou kear tkt o/arm bill or ~ou smell smoke, stop C/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.</Paragraph>
    <Paragraph position="4"> PLAN uses goal-sensitive rules to impose an ordering on this set of fragments. A typical planning rule is: &amp;quot;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'.</Paragraph>
    <Paragraph position="5"> FILTER, deletes from the set, ell propositions currently represented as known by the hearer.</Paragraph>
    <Paragraph position="6"> HILL-CLIMB coordinates two sub-activities: AGGREGATOR applies rules to combine two or three fragments into a single one. A typical aggregation rule is: &amp;quot;The two fragments 'x does A' and 'x does B' can be combin~! into a single fragment: 'x does A and B'&amp;quot;.</Paragraph>
    <Paragraph position="7"> PREFERENCER evaluates each proposed new fragment, producing a numerical measure of its &amp;quot;goodness&amp;quot;. A typical preference rule is: &amp;quot;When instructing the hearer, lncremm the accumulating measure by 10 for each occurrence of the symbol 'YOU'&amp;quot;.</Paragraph>
    <Paragraph position="8"> 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.</Paragraph>
    <Paragraph position="9"> 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.  The aggregation and preference rules are directly analogoua to the capabilities of linguistic eempotence and performance, respectively.</Paragraph>
    <Paragraph position="10"> OUTPUT lsa simple (two pages of LISP) text generator driven by a context free grammar.</Paragraph>
  </Section>
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