USER MODELLING, DIALOG STRUCTURE, AND 
DIALOG STRATEGY IN RAM-ANS 
Katharina Morik 
Technische Universitaet Berlin 
Project group KIT , Sekr. FR 5-8 
Franklinstr. 28/29 
D-IO00 Berlin i0 (Fed. Rep. Germany) 
ABSTRACT 
AI dialog systems are now developing from 
question-answering systems toward advising 
systems. This includes: 
discussed here, but see (Jameson, Wahlster 1982). 
The second part of this paper presents user 
modelling with respect to a dialog strategy which 
selects and verbalizes the appropriate speech act 
of recommendation. 
- structuring dialog 
- understanding and generating a wider range of 
speech acts than simply information request and 
answer 
- user modelling 
User modelling in HAM-ANS is closely connected to 
dialog structure and dialog strategy. In advising 
the user, the system generates and verbalizes 
speech acts. The choice of the speech act is 
guided by the user profile and the dialog strategy 
of the system. 
INTRODUCTION 
The HAMburg Application-oriented Natural language 
System (HAM-ANS) which has been developed for 3 
years is now accomplished. We could perform 
numerous dialogs with the system thus determining 
the advantages and shortcomings of our approach 
(Hoeppner at al. |984). So now the time has come 
to show the open problems and what we have learned 
as did the EUFID group when they accomplished 
their system (Templeton, Burger 1983). This paper 
does not evaluate the overall HAM-ANS but is 
restricted to the aspect of dialog structuring and 
user modelling. 
Dialog structure is represented at two levels: the 
outline of the dialog is explicitly represented by 
a top (evel routine, embedded sub-dialogs are a 
result of the processing strategy of HAM-ANS. The 
overall dialog structure is utilized for 
determining the appropriate degree of detail of 
the referential knowledge for a particular dialog 
phase. The embedded sub-dialogs refer to other 
knowledge sources than the referential knowledge. 
In the first part of this paper dialog structuring 
in HAM-ANS is described. Handling of dialog 
phenomena as ellipsis and anaphora is not 
DIALOG STRUCTURE 
In one of its three applications HAM-ANS plays the 
role of a hotel clerk who advises the user in 
selecting a suitable room. The task of advising 
can be seen here as a comparison of the demands 
made on an object by the client and the advisor's 
knowledge about available objects, performed in 
order to determine the suitability of an object 
for the client. Dialogs between hotel clerks at 
the reception and the becoming hotel guest are 
usually short and stereotyped but offer some 
flexibility as well because the ~ruests do not 
establish a homogeneous group. With recourse to 
this restricted dialog type we modelled the 
outline of the dialog. Dialog structure is not 
represented in terms of some actions the user 
might want to perform as did Grosz (1977), Allen 
(1979), Litman,Allen (1984) nor in terms of 
information goals of the user as did Pollack 
(1984), but we represent and use knowledge about a 
dialog type. For formal dialogs in a well defined 
comunication setting this is possible. For a 
practical application the dialog phases and steps 
should be empirically determined. We do not 
consider the hotel reservation situation an 
example for real application. We just wanted to 
show the feasibility of recurring to linguistic 
knowledge about types of texts or dialogs. Real 
clerk - guest dialogs show some features we did 
not concern. Features of informal man-man- 
communication as, e.g., narratives and role- 
defining utterances of dialog partners were 
excluded from the model of the dialog. Man- 
machine-interaction is seen as formal as opposed 
to informal communication, and there is no way of 
redefining it as personal talk. 
The outline of the dialog is a structure at three 
different levels: there are three dialog phases, 
each consisting of several dialog steps (see 
Fig. l). Each dialog step can be performed by 
several dialog acts. 
* The work on HAM-ANS has been supported by the 
BMFT (Bundesministerium fuer Forschung und 
Technologic) under contract 08it15038. 
Although the outline of the dialog is fixed, there 
is also flexibility to some extent: 
268 
GREETING 
FINDING OUT WHAT THE USER WANTS 
CONFIRMATION 
RECOMMENDING A ROOM 
\ 
giving the initiative \ 
ANSWERING QUESTIONS ABOUT THAT PARTICULAR ROOM 
/ 
taking the initiative 
BOOKING THE ROOM (OR NOT) 
:GOOD-BYE 
Fig. l outline of the dialog 
- The dialog step "Finding out what the user 
wants" consists of as many questions of the 
system as are necessary 
- If the confirmation step does not succeed it is 
jumped back to the dialog act where the user 
initializes the dialog step "Finding out what 
the user wants 
- In the dialog phase concerning a particular 
room the system asks for regaining the 
initiative. If the user denies the questioning 
phase is continued. 
The advantages of fully utilizing knowledge about 
the dialog type are the reduction of complexity, 
i.e. the system does not have to recognize the 
dialog step, realistic response time because no 
additional processing has to be done for planning 
the dialog, and the explicit representation of the 
dialog structure. The declarative representation 
of dialog structure allows for modelling different 
degrees of detail of the world knowledge attached 
to the dialog phases. 
Views of the domain 
We believe that the degree of detail of 
referential knowledge is constituing a dialog 
phase. In other words, different degrees of detail 
or abstraction seperate dialog phases. Reichman 
could have made this point, because her empirical 
data do support this observation (Reichman 1978). 
In focus is not only a certain portion of a task 
tree or a certain step in a plan, but also a 
certain view of the matter. Therefore, attached 
to the dialog phases are different knowledge bases 
accessable in these phases. World knowledge 
contains for the first and the last phase overview 
knowledge about the hotel and its room categories, 
for the second phase detailed knowledge about one 
instance of a room category, i.e. a particular 
room. 
The room categories are derived from the 
individual rooms by an extraction process which 
disregards location and special features of 
objects as, e.g., colour. But representing 
overview knowledge is not just leaving out some 
types of arcs in the referential semantic network! 
One capability of the extraction process is to 
group objects together if there is an available 
word to identify the group and to identify objects 
which are members of the group not just parts of a 
whole. An example may clarify this. 
One advantage of some of the rooms is that they 
have a comfortable seating arrangement made up of 
various objects: couch, chairs, coffee table, etc. 
HAM-ANS can abstract from this grouping of objects 
and identify it as a "Sitzecke" - a kind of cozy 
corner, a common concept and an every-day word in 
German. Another example of a group is the concept 
"Zimmerbar" (room bar) consisting of a 
refrigirator, drinking glasses and drinks. 
Another difference between overview knowledge and 
detailed knowledge is that some properties of 
objects are inherited to the room category. For 
example, what can be seen out of the window is 
abstracted to the view of the room category. A 
room category has a view of the Alster, one of the 
two rivers of Hamburg, if at least one window 
faces the Alster. 
While selecting a suitable room for the user the 
system accesses the abstracted referential 
knowledge. Not until the dialog focuses on one 
particular room, does the system answer in detail 
questions about, e.g. the location of furniture, 
its appearance, comfort,etc. Thus different 
degrees of detail are associated with different 
dialog phases because the tasks for which the 
referential knowledge is needed differ. The link 
between the overview information, e.g. that a room 
category has a desk, a seating arrangement 
("Sitzecke") etc., and the detailed referential 
knowledge about a particular room of that 
category, e.g., that there is a desk named DESKI, 
that there are three arm chairs and a coffee table 
etc., is established by an inverse process to the 
extraction process. This inverse process finds 
tokens or derives implicit types, for which in 
turn the corresponding tokens are found. When 
initiative is given to the user, the tokens of 
objects mentioned in the preceeding dialog are 
entered into the dialog memories which keep track 
of what is mutually known by system and user. 
Thus, if the seating arrangement ("Sitzecke") has 
been introduced into the dialog the user may ask, 
where "the coffee table" is located using the 
definite description, because by naming the 
seating arrangement the coffee table is implicitly 
introduced. 
The procedural connection between overview and 
detailed knowledge entails, however, a problem. 
First, while semantic relations between concepts 
are represented in the conceptual network thus 
determining noun meaning, the meaning of "group 
nouns" could not be represented in the same 
formalism. Second, inversing the extraction 
process and entering tokens into a dialog memory 
269 
leads to a problem of ambiguous referents. If a 
"Sitzecke" has been mentioned - which arm chairs 
or couchs are introduced and how many? The system 
may infer the tokens, but not the user. For him, a 
default description of a "Sitzecke", which is 
concretized only if an object is named by the 
user, should be entered into the dialog memory. 
Subdialogs 
We have seen the outline of the dialog, but also 
inside the questioning phase there is a dialog 
structure. The system initiates a clarification 
dialog if it could not understand the user input. 
This could be, for instance, a lexicon update 
dialog. The user may start a subdialog in putting 
a meta-question as, e.g., "What was my last 
question?" or "What is meant by carpet?". Maim- 
questions are recognized by clue patterns. Here, 
too, attached to the subdialogs are different 
knowledge sources: subdialogs are not referring 
to the referential knowledge (about a particular 
room) but to the lexical update package, the 
dialog memories, or the conceptual knowledge. 
Subdialogs are embedded dialogs which can be seen 
in the system behavior regarding anaphora, for 
instance. They are processed in bypassing the 
normal procedure of parsing, interpretation and 
generating. This solution should be replaced by a 
dialog manager module which decides as a result of 
the interpretation process which knowledge source 
is to be taken as a basis for finding the answer. 
USER MODELLING 
User modelling in AI most often concerns the 
user's familiarity with a computer system (Finin 
1983, Wilensky 1984) or his/her knowledge of the 
domain (Goldstein 1982, Clancey 1982, Paris 1983). 
These are, of course, important aspects of user 
modelling, but the system must in addition model 
the user-assessment aspect. 
Value judgements 
The claim of philosophers and linguists (Hare 
1952, Grewendorf 1978, Zillig 1982) that value 
judgements refer to some sort of an evaluation 
standard are not sufficient. In AI, the questions 
are: 
- how to recognize evaluation standards 
- how to represent them 
- how to use them for generating speech acts 
evaluations should be used to select those objects 
which might interest the user. For example, which 
information about a hotel room is presented to the 
user depends on the interests of the user and his 
requirements, which can be inferred from his/her 
evaluation standard. The information to be 
outputted can be selected on the basis of user's 
requirements rather than on the basis of freedom 
of redundancy given the user's knowledge. Thus, 
the choice of the relevant objects as well as the 
choice of the appropriate value judgement requires 
the modelling of the user's evaluation standards. 
A system which performs recommendations of fiction 
books is Rich's GRUNDY (Rich 1979). The basis 
heuristic underlying the consultative function is 
that people are interested in books in which 
characters have the same type of personality as 
they themselves have, or where characters are 
facing a situation similar to their o~en. 
Therefore, recognizing the personality type of a 
user is a central concern for GRUNDY and can be 
used directly for the evaluation of books. We'll 
see that for HAM-ANS the utilization of knowledge 
about the user is not so straightforward. Neither 
is HAM-ANS interested in the personality type of 
the user nor is there any plausible direct match 
between personality type and room category. We 
want to distinguish the user facts, which is 
knowledge about the user and his wants given 
explicitly by himself, the user profile, which is 
knowledge about the user inferred by the system, 
and the interest profile, which is the user's 
evaluation standard as it is inferred by the 
system. One has to be inferred from the other. 
Building the interest profile 
Let us look at an example of the first dialog 
phase: 
SYS : 
USER: 
SYS : 
USER: 
SYS : 
USER: 
SYS : 
\[t is well known that recommendations rely on the 
presumed evaluation standards of the hearer. If 
these speech acts are to be actually implemented USER: 
in the system and not just read into the system 
responses by the user, the system must be able to SYS : 
reco~ni=e the user's stRndards of evaluation, 
derive the demands placed by the user on objects 
or situations which form the subject with the USER: 
demands. \[t would be overinformative to output 
evaluations about every possible object. Rather, SYS : 
HOTEL ALSTERBLICK, GUTEN ABEND! 
At the sign of Hotel Alsterblick, good 
even ing' 
Hallo hier Doktor Heine von IBM Heidelberg, 
Hello this is Doctor Heine from IBM 
Heidelberg 
WAS KANN ICH FUER SIE TUN? 
What can I do for you? 
Ich moechte ein Doppelzimmer mit Bad fuer 
zwei Tage. 
I would like a single room with bath for two 
days. 
UND AB WANN WIRD DAS ZIMMER BENOETIGT? 
And for when would you like the room? 
Ab morgan 
For tomorrow 
UND AUF WELCHEN NAMEN SOLL DAS ZI~4ER 
GEBUCHT WERDEN? 
And in whose name should the room be booked? 
Auf meinen. 
In mine. 
EIN DOPPELZIMMER BIS Zt~1 25.4. HAETTEN SIE 
GERN? 
You would like a double room until april 25? 
Ja. 
Yes. 
UNSER HOTEL WIRD IHNEN GEFALLEN UND WIR 
270 
HABEN AUCH NOCH EIN ZIMMER FREI. DAS ZIMMER 
HAT KEIN FERNSEHGERAET ABER EINE ZI~@4ERBAR, 
EINEN SCHREIBTISCH, EIN TELEFON UND EINE 
SITZECKE. HABEN SIE ZU DIESEM ZIMMER NOCH 
EINE FRAGE? 
You will enjoy our hotel and there is still 
a room available. The room has no TV but a 
room bar, a desk, a telephone, and a seating 
arrangement. Do you have questions 
concerning this room? 
In this dialog phase the user first informs the 
system about his concerns and indirectly give some 
hints about his standards. The facts, i.e. from 
when to when what kind of a room should be booked 
for whom, are stored in a data structure under the 
heading GUEST. If the user has not given all the 
necessary information, the system asks for it. 
The way the user introduces himself may give a 
hint as to what kind of user he is. But the system 
would not ask for title or firm or location if the 
user has not volunteered the information. From 
these data some inference processes are initiated, 
estimating independently profession (here, 
manager) financial status (here, rich), and 
purpose of trip (here, transit). The estimations 
are stored under the heading SUPPOSE. They can be 
viewed as stereotypes in that they are 
charcteristics of a person, relating him/her to a 
certain group to which a number of features are 
assigned (Gerard, Jones 1967). As I mentioned 
earlier the application of stereotypes is not as 
straightforward as in Rich's approach. Two steps 
are required. We've just seen the first step, the 
generation of SUPPOSE data. As opposed to GUEST 
data, the SUPPOSE ata are not certain, thus 
supposed data and facts are divided. 
In the second step, each of the SUPPOSE data 
independently triggers inferences, that derive 
requirements presumably placed on a room and on 
the hotel by the user. The requirements are 
roughly weighed as very important, important and 
surplus (extras). If the same requirement is 
derived by more than one inference and with 
different weights, the next higher weight is 
created or the stronger weight is chosen, 
respectively. This is, of course, a rather 
simplified way of handling reinforcement. But a 
more finely treatment would yield no practical 
results in this domain. The requirements for the 
room category and for the hotel are stored 
seperately in semantic networks. An excerpt of the 
networks corresponding to the dialog example 
above: 
((WICHTIG (HAT Z FERNSEHGERAET).I) 
((SEHR-WICHTIG (HAT Z TELEFON).I) 
((SEHR-WICHTIG (HAT Z SCHREIBTISCH).I) 
((SURPLUS (HAP HOTEL1 FREIZEITANGEBOT-2).I) 
((SEHR-WICHTIG (IST HOTEL1 IN/ ZENTRALER/ LAGE).I) 
The requirements are then tested against the 
knowledge about the hotel and the room categories. 
Some requirements correspond directly to stored 
features. Others as here, for instance, the 
leisure opportunities nt~mber 2 or the central 
location of the hotel are expanded to some 
features by inference procedures. Thus here, too, 
there is an abstraction process. The requirements 
together with their expansions represent the 
concretized evaluation standard of the user. They 
are called the interest profile of the user. 
Generating recommendations 
Now, let's see what the system does with this. 
First, it matches the requirements against the 
room or hotel features thus yielding an evaluation 
from every room category of the requested kind 
(here, double room). The evaluation of a room 
category consists of two lists, the one of the 
fulfilled criteria and the one of the unfulfilled 
criteria. 
Secondly, based on this evaluation speech acts are 
selected. The speech act recommendation is split 
up into STRONG RECO~NDATION, WEAK RECO~4EN- 
DATION, RESTRICTED RECOMMENDATION, and NEGATIVE 
RECO~NDATION. The speech acts as they are known 
in linguistics are not fine grinned enough. Having 
only one speech act for recommending would leave 
important information to the propositional 
content. The appropriate recommendation is chosen 
according to the following algorithm: 
- if all the criteria are fulfilled, a STRONG 
RECO~NDATION is generated 
- if no criteria are fulfilled, a NEGATIVE 
RECOMMENDATION is generated 
- if all very important criteria are fulfilled, 
but there are violated criteria, too, a 
RESTRICTED RECOM~NDATION is generated 
- if there are some criteria fulfilled, but even 
very important criteria are violated, a WEAK 
RECO~NDATION is generated 
This process is executed both for the possible 
room categories and for the hotel. The resutt is a 
possible recommendation for each room category and 
the hotel. Out of these possible recommendations 
the best choice is taken. 
Third, a rudimentary dialog strategy selects the 
most adequate speech act for verbalization. For 
instance, if there is nothing particularly good to 
say about a room but there are features of the 
hotel to be worth landing, then the hotel will be 
recommended. The hotel recommendation is only 
verbalized, iff it suits perfectly and the best 
possible recommendation for a room category is not 
extreme, i.e. neither strong nor negative. The 
negative recommendation has priority over the 
hotel recommendation because an application- 
oriented system should not persuade a user nor has 
a goal for its own - although this is an 
interesting matter for experimental work and can 
be modelled within our framework. 
In our example dialog the best recommendation of a 
room category is the restricted recommendation for 
room category 4. The hotel fulfills all the 
inferred requirements and can be recommended 
strongly. These speech acts have to be Verbalized 
now. For verbalization, too, a dialog strategy is 
271 
applied: 
The better the evaluation 
the shorter the recommendation. 
The most positive recommendation is realized as: 
DA HABEN WIR GENAU DAS PASSENDE ZI~9~ER 
FREI. 
(We have just the room for you.) 
FUER SIE 
In our example the recommendation can't be so 
short, because the disadvantages should be 
presented to the user so that he can decide 
whether the room is sufficient for his 
requirements. Therefore, the room category is 
described. Among all the features of the room only 
those are verbalized which correspond to the 
user's interest profile. In order to verbalize 
the restricted recommendation, a "but" construct 
of the internal representation language SURF of 
HAM-ANS is built (Fig. 2). 
From this structure the HAM-ANS verbalization 
component creates a verbalized structure which is 
then transformed into a preterminal string from 
which the natural language surface is built and 
then outputted (Busemann 1984). The verbalization 
component includes the updating of the dialog 
memories. 
~af-d: IS 
(t-s: (q-d: D-) (lambda: x4 (af-a: ISA x4 
ZI~R))) 
(lambda: x4 
(af-a: HAT x4 
(t-o: BUT 
(t-s: (q-qt: KEIN) (lambda: x4 (af-a: ISA x4 
FERNSEHGERAET))) 
(t-o: AND 
(t-s: (q-qt: E-) (lambda: x4 (af-a: ISA x4 
ZIMMERBAR))) 
(t-o: AND 
(t-s: (q-qt: E-) (lambda: x4 (af-a: ISA x4 
SCHREIBTISCH))) 
(t-o: AND 
(t-s: (q-qt: E-) (lambda: x4 (af-a: ISA x4 
TELEFON))) 
(t-s: (q-qt: E-) (lambda: x4 (af-a: ISA x4 
SITZECKE)))))))))) 
Fig.2 SURF structure 
After the dialog strategy has selected the most 
posltive recommendation it can fairly give 
regarding the evaluation form of the room category 
which suits best the (implicit) demands of the 
user and has chosen the appropriate formulation 
for the recommendation, it prepares to give the 
initiative to the user thus entering the 
questioning dialog phase. That is: it is focused 
on the selected room category and the more 
detailed data about an instance of that particular 
room category are loaded. In our example, the 
referential network and the spatial data of room 4 
are accessed. 
OPEN QUESTIONS 
The problems that have yet to be solved may be 
divided into three groups: those that could be 
solved within this framework, those that require a 
change in system architecture and those that are 
of principle nature. 
A problem which seems to fit into the first group 
is the explanation of the suppositions. The user 
should get an answer to the questions: 
Who do you think I am? 
How do you come to believe that I am a manager? 
How do you come to believe that I need a desk? 
The first question may be answered by verbalizing 
the SUPPOSE data. The second and the third 
question must be answered on the basis of the 
inferences taking into account the reinforcement 
as did Wahlster (1981). 
The third question may be a rejection of the 
supposed requirement rather than a request for 
justification or explanation. Understanding 
rejections of supposed requirements includes the 
modification of the requirement networks. For 
example, the user could say after the restricted 
recommendation: 
But I don't need a TV! 
Then the room category 4 fits perfectly well and 
may be strongly recommended. 
Or the user could state: 
But I don't want a desk. I would like to have a 
TV instead. 
In this case the requirement net of the room 
categories is to be changed: 
REMOVE ( ? (HAT Z DESK)) 
ADD (VERY-IMPORTANT (HAT Z TV)) 
With this the room categories have to evaluated 
again and perhaps another room category will then 
be recommended. 
A type of requirements that is yet to be modelled 
is the requirement that something is not the case. 
For example, the requirement that there should be 
no air-conditioning (because it's noisy). 
A change in system architecture is required if the 
advising is to be integrated into the questioning 
phase. The reason why this is not possible by now 
is, for one part, of practical nature: memory 
capacity does not allow to hold overview knowledge 
and detail knowledge at once. The other part, 
however, is the increase of complexity. Questions 
are then to be understood as implicit 
requirements. For example: 
The room isn't dark? 
ADD ( IMPORTANT ( IST Z BRIGHT)) 
The hard problem we are then confronted with is an 
272 
instance of the frame problem (Hayes 1971:495, 
Raphael 1971). When does the overall evaluation of 
a room category not hold any longer? When are all 
the room categories to be evaluated again 
according to a modified interest profile? When 
should it be switched from one selected room 
category to another? These are problems of 
principle nature which have yet to be solved. 
Further research is urgently needed. 
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