<?xml version="1.0" standalone="yes"?>
<Paper uid="C94-2197">
  <Title>A Bayesian Approach for User Modeling in Dialogue Systems</Title>
  <Section position="3" start_page="0" end_page="7272" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Recently many researchers have pointed out that user modeling is important in the study of (tiMog systems. User n:o&lt;h!ling does not just render a dialog syst(,nl more cooperative, lint constitutes an indis1)ensable l)rerequisite fin&amp;quot; any flexible (lialog in a wider &lt;tomain\[9\]. The user models interact closely with all other components of the system and often cannot easily be separated from them. For examph,, the inl)ut anMysis component refers to tile user's knowledge to solve referentiM ambiguities, and tile output generation component does the same for h,xical el,oices.</Paragraph>
    <Paragraph position="1"> The con&lt;:epts are usually explained l&gt;y showing their relations to the other known concepts. Thus, for the &lt;lialog system it is important to guess what the user knows (user's knowledge) in order to explain new concel)ts in terms of know,t concepts. For examl/le , con: sider that tit(, system explains the location of a restaurant to the user. It might 1)e useless to tell the. user the position in terms of the Mlsolute &lt;:oordinate system, since the user's mental model is not based on the absolute coordinate. Therefore, the system should show the relative location frmn the lo(:ation tit(' user alrea(ly knows. It is difficult to predict which locations the user, who l)erhaps is a stranger to the system, knows.</Paragraph>
    <Paragraph position="2"> Though the syst:em &lt;:ouhl atteml)t to a('quire the information l/y asking the user al)out her k,towle(lge, too many questions may irritate the user. Such a system is considered mechanical and not helpful. Therefore, tit(&amp;quot; system is required to guess the user's knowledge by finding clues in the user's utterance and to refine the user's model incrementally.</Paragraph>
    <Paragraph position="3"> In the user modeling component of UC\[5\], several stereotyped user models which vary the user's level of expertise were prepared beforehand and the appropriate model was selected based o1: the user's utterances. Ill the approach used by Wallis and Shortlifl'e \[12\], the expertise h,vel was assigned to all concepts in the user model. The system guessed the user's level, and the concepts with the expertise level lower than her level are considered to be known by her. This n:o(lel can deal with tit(.' level of expertise more appropriately than UC, because the system does not have to prepare the nmltiple user nlodels for each expertise h, vel.</Paragraph>
    <Paragraph position="4"> The approach of pr&lt;.'paring several user models and adoptit,g one, however, is an al&gt;l&gt;roximation of user modeling. The expertise level of tit(: user is continuous and, in general, the unique measuremelfl: of expertise level is not appropriate for some domMns, specifically the domain of town guidance consi&lt;lere&lt;l in this paper, because the areas that are known differ with the users.</Paragraph>
    <Paragraph position="5"> Another problem of user modeling is updating the nmdel as the (tialog progresses. At the beginning of the diMogue the system cannot expect the user nm&lt;M to be accurate. As the diMogue progresses the. system can acquire clues of the user's knowledge fl'om his utteranees. Also, the system can assume that the concepts mentioned are known to the user. Thus. updating the user model shouhl 1)e performed incrementally.</Paragraph>
    <Paragraph position="6"> One difficulty of updating user nmdels is dealing with uncertainties. The clues that can be obtained from the user's utterances are uncertain, the iltfol'nlatiol( may conlli&lt;:t with what has been hi,rained, and, as a result, the user mo&lt;lel may be revised. The effects of the systtnn's explanation are also uncertain. Furthermore, reasoning about the user's kuowledge must be performed Oil the basis of uncertainties. Most previous apl)roaches to this prolflem are rule-based metho(ts.</Paragraph>
    <Paragraph position="7"> Cawsey \[2\] sorted the update rules in order of their reliability and applied them in this order. In another approach, tit(., mechanisnl such as TMS\[6\] or nomnonotonic logic\[l\], is used to maintain the consistency of  |;he 211odcl. I(; SCCliIS that rule,-l),tse(\[ aLl)l)ro~t(:hes h~tve a pol;entiM defect for dealing with unt:ertMnties\[4\]. The Bayesian al)proa(:h ca, n (leM wil;h bol;h un(:erta.in (ambiguous) evidences and uncertain re~Lsoning sl;raightforwardly. null In this pat)or , wc t)roposc ;~ prol)nhilistic ~l)l/ro~tch for user modeling ill dialog systems. The Bayesian networks ;tre Itsc(l to rel)re.sent the user's knowledge and (Ir~tw inferen(:es froni that, ~trt(l provide the fine-grahwxl solutioils to the ln'ol)lems l/reviously mcntiol,ed. In Sl)ite of the pol:entiM ;t(lwud;;tge of I;he Bayesi;Ln al)-I/ro~ch, l;her(~ are few attenq)ts to mnploy it in user modeling.</Paragraph>
    <Paragraph position="8"> The adva.nt;ages of the Bayesian ;q)l)roach over the rule-1);tsed ;q)l)roa(:h are ~ts follows. First, rules for updating nscr models are not necessary. C;twsey \[2\] 1)oiuted out; there are four lmdn sources of informal;ion l;hat can be used to up(l;tte tim user model wh;~t, lahe user s;~ys ~tnd asks, what the .~ysl;em l;ells I;he user, 1,11('. leve.l of exl)ertise of the user, and rel:d;ionshit)s I)\[!tween con(:el)l;s in the domain. '\['hey c~tli l)(! incorl)oratt(~d ill the tel)resented.ion of \]~tyesian nel;works au(l can be used to Ul)(lal:e the user m.(lel 1)y (,v;duacting the networks.</Paragraph>
    <Paragraph position="9"> Second, the ll~yesian network t)rovidcs more de..</Paragraph>
    <Paragraph position="10"> tailed infi)rmal;ion of users' knowledge, hi t,he (:;tse of l)imtry modeling of knowh~(lge, whe.reby (tither the user klmws or does llO~ kllow ~1, c(}iic(}p\[;~ i{; is too co3J',qc to judge the model under mlccrl:Mnl:y. Therefl)rc, usually, the degree of I)elief is ;tssigned t.o M1 (:on(:etyts iu the model. It is nol; (:leau' where the degree of belief comes from or wharf; it llIC;Lll.q. ()ll tim or:her h;~nd, how~,.ver, l.h(', lbLy(!sian ,tf)l)ro;~(:h provides I:he (lel~r(~(! of belie\[ for cle~u' semantics, which is 1)rohal~ility.</Paragraph>
    <Paragraph position="11"> The relnMnder of I;his pap(w is organized ill four se(:ti(lltS. Section 2 is devoted to an oul.linc of l~a.ye,'d;m networks. ,qection 3, knowledge represental;iou in terms of \]btyesian uctworks is discussed. If the model is once represeul;e(l, then l;he upd;d;hl\[~ of t.he model will 1)(! taken (:are of t.hrough the ev;du;~tion of the network. ,qe(:tion 4, some exanllfles ;cre given Mon K with lilt (!xl)eriu~ent; to show the lt(lvlLill;~tge (if o/lr al)tlro~tch. Section 5 concludes this l);q)cr.</Paragraph>
  </Section>
class="xml-element"></Paper>