Dialog Control in a Natural Language System 1 
Michael Gerlach Helmut Horacek 
Universit~t Hamburg Fachbereich Informatik Projektgruppe WISBER 
Jungiusstra6e 6 D-2000 Hamburg 36 F.R.G. 
ABSTRACT 
In this paper a method for controlling 
the dialog in a natural language (NL) 
system is presented. It provides a deep 
modeling of information processing 
based on time dependent propositional 
attitudes of the interacting agents. 
Knowledge about the state of the dialog 
is represented in a dedicated language 
and changes of this state are described 
by a compact set of rules. An appropri- 
ate organization of rule application is 
introduced including the initiation of 
an adequate system reaction. Finally 
the application of the method in an NL 
consultation system is outlined. 
INTRODUCTION 
The solution of complex problems fre- 
quently requires cooperation of multi- 
ple agents. A great deal of interaction is 
needed to identify suitable tasks whose 
completion contributes to attaining a 
common goal and to organize those 
tasks appropriately. In particular, this 
involves carrying out communicative 
subtasks including the transfer of 
knowledge, the adjustment of beliefs, 
expressing wants and pursuing their 
satisfaction, all of which is motivated 
by the intentions of the interacting 
agents \[Werner 88\]. An ambitious dia- 
log system (be it an interface, a mani- 
pulation system, or a consultation sys- 
tem) which is intended to exhibit (some 
of) these capabilities should therefore 
consider these intentions in processing 
1 The work described in this paper is part 
of the joint project WISBER, which is sup- 
ported by the German Federal Ministery for 
Research and Technology under grant ITW- 
8502. The partners in the project are: Nixdorf 
Computer AG, SCS Orbit GmbH, Siemens 
AG, the University of Hamburg, and the Uni- 
versity of SaarbrOcken. 
the dialog, at least to the extent that is 
required for the particular type of the 
dialog and the domain of application. 
A considerable amount of work in cur- 
rent AI research is concerned with in- 
ferring intentions from utterances (e.g., 
\[Allen 83\], \[Carberry 83\], \[Grosz, Sidner 
86\]) or planning speech acts serving 
certain goals (e.g., \[Appelt 85\]), but only 
a few uniform approaches to both as- 
pects have been presented. 
Most approaches to dialog control de- 
scribed in the literature offer either 
rigid action schemata that enable the 
simulation of the desired behavior on 
the surface (but lack the necessary de- 
gree of flexibility, e. g., \[Metzing 79\]), or 
descriptive methods which may also in- 
clude possible alternatives for the con- 
tinuation of the dialog, but without ex- 
pressing criteria to .~aide an adequate 
choice among them (e. g., \[Me~ing et al. 
87\], \[Bergmann, Gerlach 87\]). 
Modeling of beliefs and intentions (i.e., 
of propositional attitudes) of the agents 
involved is found only in the ARGOT 
system \[Litman, Allen 84\]. This ap- 
proach behaves sufficiently well in se- 
veral isolated situations, but it fails to 
demonstrate a continuously adequate 
behavior in the course of a complete dia- 
log. An elaborated theoretical frame- 
work is provided in \[Cohen, Perrault 
79\] but they explicitly exclude the dele- 
tion of propositional attitudes. Hence, 
they cannot explain what happens 
when a want has been satisfied. 
In our approach we have enhanced the 
propositional attitudes by associating 
them with time intervals expressing 
their time of validity. This enables us to 
represent the knowledge about the ac- 
tual state of the dialog (and also about 
past states) seen from the point of view 
of a certain agent and to express 
changes in the propositional attitudes 
- 27 - 
occurring in the course of the dialog 
and to calculate their effect. This deep 
modeling is the essential resource for 
controling the progress of the conversa- 
tion in approaching its overall goal, 
and, in particular, for determining the 
next subgoal in the conversation which 
manifests itself in a system utterance. 
We have applied our method in the NL 
consultation system WISBER (\[Hora- 
cek et al. 88\], \[Sprenger, Gerlach 88\]) 
which is able to participate in a dialog 
in the domain of financial investment. 
REPRESENTING 
PROPOSITIONAL ATTITUDES 
Knowledge about the state of the dialog 
is represented as a set of propositional 
attitudes. The following three types of 
propositional attitudes of an agent 
towards a proposition p form a basic 
repertoire : 
KNOW : The agent is sure that p is true. 
This does not imply that p is really true 
since the system has no means to find 
out the real state of the world. As- 
suming that the user of a dialog system 
obeys the sincerity condition (i.e., al- 
ways telling the truth, c.f. \[Grice 75\]) 
an assertion uttered by the user implies 
that the user knows the content of that 
assertion. 
BELIEVE : The agent believes, but is not 
sure, that p is true, or he/she assumes p 
without sufficient evidence. 
WANT : The agent wants p to be true. 
Propositional attitudes are represented 
in our semantic representation lan- 
guage IRS, which is used by all system 
components involved in semantic-prag- 
matic processing. IRS is based on predi- 
cate calculus, and contains a rich collec- 
tion of additional features required by 
NL processing (see \[Bergmann et al. 87\] 
for detailed information). A propositio- 
nal attitude is written as 
(<type> <agent> <prop> <time>): 
• <type> is an element of the set: 
KNOW, BELIEVE, and WANT. 
• The two agents relevant in a dialog 
system are the USER and the SYSTEM. 
In addition, we use the notion 
'mutual knowledge'. Informally, 
this means that both the user and 
the system know that <prop> is 
true, and that each knows that the 
other knows, recursively. We will 
use the notation (KNOW MUTUAL 
< prop > ...) to express that the pro- 
position < prop > is mutually known 
by the user and the system. 
• < prop > is an IRS formula denoting 
the proposition the attitude is about. 
It may again be a propositional atti- 
tude, as in (WANT USER (KNOW USER x 
...) ...) which means that the user 
wants to know x. The proposition 
may also contain the meta-predi- 
cates RELATED and AUGMENT: (RELATED 
x) means 'something which is related 
to the individual x', i.e., it must be 
possible to establish a chain of links 
connecting the individual and the 
proposition. In this general form 
RELATED iS only used to determine 
assumptions about the user's compe- 
tence. For a more intensive applica- 
tion, however, further conditions 
must be put on the connecting links. 
(AUGMENT 0 means 'something more 
specific than the formula f', i.e., at 
least one of the variables must be 
quantified or categorized more 
precisely or additional propositions 
must be associated. These meta-pre- 
dicates are used by the dialog con- 
trol rules as a very compact way of 
expressing general properties of pro- 
positions. 
• Propositional attitudes as any other 
states hold during a period of time. 
In WISBER we use Allen's time 
logic \[Allen 84\] to represent such 
temporal information \[Poesio 88\]. 
<time> must be an individual of 
type TIME-INTERVAL. In this paper, 
however, for the sake of brevity we 
will use almost exclusively the spe- 
cial constants NOW, PAST and FUTURE, 
denoting time intervals which are 
asserted to be during, before or after 
the current time. 
INFERENCE RULES 
As new information is provided by the 
user and inferences are made by the 
system, the set of propositional atti- 
tudes to be represented in the system 
will evolve. While the semantic-prag- 
matic analysis of user utterances ex- 
ploits linguistic features to derive the 
- 28 - 
attitudes expressed by the utterances 
(c.f. \[Gerlach, Sprenger 88\]), the dialog 
control component interprets rules 
which embody knowledge about know- 
ing and wanting as well as about the 
domain of discourse. These rules de- 
scribe communicative as well as r/on- 
communicative actions, and specify 
how new propositional attitudes can be 
derived. Rules about the domain of dis- 
course express state changes including 
the involved action. The related states 
and the triggering action are associated 
with time-intervals so that the correct 
temporal sequence can be derived. 
Both classes of rules are represented in 
a uniform formalism based on the sche- 
ma precondition - action - effect: 
• The precondition consists of patterns 
of propositional attitudes or states 
in the domain of discourse. The pat- 
terns may contain temporal restric- 
tions as well as the meta-predicates 
mentioned above. A precondition 
may also contain a rule description, 
e.g., to express that an agent knows 
a rule. 
• The action may be either on the lev- 
el of communication (in the case of 
speech act triggering rules) or on the 
level of the domain (actions the dia- 
log is about). However, there are al- 
so pure inference rules in the dialog 
control module; their action part is 
void. 
• The effect of a rule is a set of descrip- 
tions of states of the world and pro- 
positional attitudes which are in- 
stantiated when applying the rule 
yielding new entries in the system's 
knowledge base. We do not delete 
propositional attitudes or other pro- 
OSitions, i.e., the system will not 
rget them, but we can mark the 
time interval associated with an en- 
try as being 'finished'. Thus we can 
express that the entry is no longer 
valid, and it will no longer match a 
pattern with the time of validity 
restricted to NOW. 
CONTROL STRUCTURE 
So far, we have only discussed how the 
actual state of the dialog (from the 
point of view of a certain agent) can be 
represented and how changes in this 
state can be described. We still need a 
method to determine and carry out the 
relevant changes, given a certain state 
of the dialog, after interpreting a user 
utterance (i.e., to decide which dialog 
rules may be tried and in which order). 
For reasons of simplicity we have divid- 
ed the set of rules into three subsets 
each of them being responsible for ac- 
complishing a specific subtask, namely: 
• gaining additional information in- 
ferable from the interrelation bet- 
ween recent information coming 
from the last user utterance and the 
actual dialog context. The combina- 
tion of new and old information may, 
e. g., change the degree of certainty 
of some proposition, i. e., terminate 
an (uncertain) BELIEVE state and cre- 
ate a (certain) KNOW state with iden- 
tical propositional content (the con- 
sistency maintenance rule package). 
• pursuing a global (cognitive or ma- 
nipulative) goal; this may be done 
either by trying to satisfy this goal 
directly, or indirectly by substitut- 
ing a more adequate goal for it and 
pursuing this new goal. In particu- 
lar, a goal substitution is urgently 
needed in case the original goal is 
unsatisfiable (for the system), but a 
promising alternative is available 
(the goal pursuit rule package). 
• pursuing a communicative subgoal. 
If a goal can not (yet) be accom- 
plished due to lack of information, 
this leads to the creation of a WANT 
concerning knowledge about the 
missing information. When a goal 
has been accomplished or a signi- 
ficant difference in the beliefs of the 
user and the system has been disco- 
vered, the system WANTS the user to 
be informed about that. All this is 
done in the phase concerned with 
cognitive goals. Once such a WANT is 
created, it can be associated with an 
appropriate speech act, provided the 
competent dialog partner (be it the 
user or an external expert) is deter- 
mined (the speech act triggerin~ 
rule package). 
There is a certain linear dependency 
between these subtasks. Therefore the 
respective rule packages are applied in 
a suitable (sequential) order, whereas 
those rules belonging to the same pack- 
- 29 - 
age may be applied in any order (there 
exist no interrelations within a single 
rule package). This simple forward in- 
ferencing works correctly and with an 
acceptable performance for the actual 
coverage and degree of complexity of 
the system. 
A sequence consisting of these three 
subtasks forms a (cognitive) processing 
cycle of the system from receiving a 
user message to initiating an adequate 
reply. This procedure is repeated until 
there is evidence that the goal of the 
conversation has been accomplished (as 
indicated by knowledge and assump- 
tions about the user's WANTS) or that 
the user wants to finish the dialog. In 
either case the system closes the dialog. 
APPLICATION IN A 
CONSULTATION SYSTEM 
In this section we present the applica- 
tion of our method in the NL consul- 
ration system WISBER involving rath- 
er complex interaction with subdialogs, 
requests for explanation, recommenda- 
tions, and adjustment of proposals. 
However, it is possible to introduce 
some simplifications typical for consul- 
ration dialogs. These are urgently need- 
ed in order to reduce the otherwise ex- 
cessive amount of complexity. In parti- 
cular, we assume that the user does not 
lie and take his/her assertions about 
real world events as true (the sincerity 
condition). Moreover, we take it for 
granted that the user is highly interest- 
ed in a consultation dialog and, there- 
fore, will pay attention to the conversa- 
tion on the screen so that it can be rea- 
sonably assumed that he/she is fully 
aware of all utterances occurring in the 
course of the dialog. 
Based on these (implicit) expectations, 
the following (simplified) assumptions 
(1) and (2) represent the starting point 
for a consultation dialog: 
(1) (BELIEVE SYSTEM 
(WANT USER 
((EXIST X (STATE X)) 
(HAS-EXPERIENCER X USER)) 
NOW) NOW) 
(2) (BELIEVE SYSTEM 
(KNOW USER 
(RELATED ((EXIST Y (STATE Y)) 
(HAS-EXPERIENCER Y USER))) 
NOW) NOW) 
They express that the user knows some- 
thing that 'has to do' (expressed by the 
meta-predicate RELATED) with states 
(STATE Y) concerning him/herself and 
that he/she wants to achieve a state 
(STATE X). In assumption 1, (STATE X) is in 
fact specialized for a consultation sys- 
tem as a real world state (instead of a 
mental state which is the general as- 
sumption in any dialog system). This 
state can still be made more concrete 
when the domain of application is taken 
into account: 
In WISBER, we assume that the user 
wants his/her money 'to be invested.' 
The second assumption expresses (a 
part of) the competence of the user. This 
is not of particular importance for many 
other types of dialog systems. In a con- 
sultation system, however, this is the 
basis for addressing the user in order to 
ask him\]her to make his/her intentions 
more precise. In the course of the dialog 
these assumptions are supposed to be 
confirmed and, moreover, their content 
is expected to become more precise. 
In the subsequent paragraphs we out- 
line the processing behavior of the sys- 
tem by explaining the application and 
the effect of some of the most important 
dialog rules (at least one of each of the 
three packages introduced in the previ- 
ous section), thus giving an impression 
of the system's coverage. In the rules 
presented below, variables are suitably 
quantified as they appear for the first 
time in the precondition. In subsequent 
appearences they are referred to like 
constants. The interpretation of the spe- 
cial constants denoting time-intervals 
depends on whether they occur on the 
left or on the right side of a rule: in the 
precondition the associated state/event 
must hold/occur during PAST, FUTURE or 
overlaps NOW; in the effect the state/ 
event is associated with a time-interval 
that starts at the reference time-inter- 
val. 
In a consultation dialog, the user's 
wants may not always express a direct 
request for information, but rather re- 
fer to events and states in the real 
world. From such user wants the sys- 
tem must derive requests for knowledge 
useful when attempting to satisfy 
them.2 Consequently the task of infer- 
- 30 - 
(KNOW MUTUAL 
(WANT USER 
(EXIST A (ACTION A)) NOW) NOW) 
A 
(KNOW SYSTEM 
(UNIQUE R 
(AND (RULE R) 
(HAS-ACTION R A) 
(HAS-PRECONDITION R 
(EXIST 51 (STATE 51))) 
(HAS-EFFECT R 
(EXIST $2 (STATE 52))))) NOW) 
=~ 
(KNOW MUTUAL 
(WANT USER 
51 NOW) NOW) 
A 
(KNOW MUTUAL 
R NOW) 
A 
(KNOW MUTUAL 
(WANT USER 
s2 NOW) NOW) 
Rule 1: Inference drawn from a user want referring to an action with unambi- 
guous consequences (pursuing a global goal) 
ring communicative goals is of central 
importance for the functionality of the 
system. 
There is, however, a fundamental dis- 
tinction whether the content of a want 
refers to a state or to an event (to be 
more precise, to an action, mostly). In 
the latter case some important infer- 
ences can be drawn depending on the 
domain knowledge about the envi- 
sioned action and the degree of preci- 
sion expressed in its specificatiqn. If, 
according to the system's domain 
model, the effect of the specified action 
is unambiguous, the user can be expect- 
ed to be familiar with this relation, so 
he/she can be assumed to envision the 
resulting state and, possibly, the pre- 
condition as well, if it is not yet ful- 
filled. Thus, in principle, a plan consist- 
ing of a sequence of actions could be cre- 
ated by application of skillful rule 
chaining. 
This is exactly what Rule 1 asserts: 
Given the mutual knowledge that the 
user wants a certain action to occur, 
and the system's knowledge (in form of 
a unique rule) about the associated pre- 
condition and effect, the system con- 
cludes that the user envisions the 
resulting state and he/she is familiar 
with the connecting causal relation. If 
the uniqueness of the rule cannot be 
2 Unlike other systems, e.g., UC \[Wilensky et 
al. 84\], which can directly perform some kinds 
of actions required by the user, WISBER is 
unable to affect any part of the real world in 
the domain of application. 
-31 
established, sufficient evidence derived 
from the partner model might be an 
alternative basis to obtain a sufficient 
categorization of the desired event so 
that a unique rule is found. Otherwise 
the user has to be asked to precise 
his/her intention. 
Let us suppose, to give an example, that 
the user has expressed a want to invest 
his/her money. According to WISBER's 
domain model, there is only one match- 
ing domain rule expressing that the 
user has to possess the money before 
but not after investing his/her money, 
and obtains, in exchange, an asset of an 
equivalent value. Hence Rule 1 fires. 
The want expressed by the second part 
of the conclusion can be immediately 
satisfied as a consequence of the user 
utterance 'I have inherited 40 000 DM' 
by applying Rule 5 (which will be ex- 
plained later). The remainder part of 
the conclusion matches almost com- 
pletely the precondition of Rule 2. 
This rule states: If the user wants to 
achieve a goal state (G) and is informed 
about the way this can be done (he/she 
knows the specific RULE R and is capable 
of performing the relevant action), the 
system is right to assume that the user 
is lacking some information which in- 
hibits him/her from actually doing it. 
Therefore, a want of the user indicating 
the intention to know more about this 
transaction is created (expressed by the 
meta-predicate AUGMENT). If the neces- 
sary capability cannot be attributed to 
the user a consultation is impossible. 
If, to discuss another example, the user 
has expressed a want aiming at a cer- 
(KNOW MUTUAL 
(WANT USER 
(EXIST S (STATE S)) NOW) NOW) 
A 
(KNOW MUTUAL 
(UNIQUE R 
(AND (RULE R) 
(HAS-EFFECT R S) 
(HAS-ACTION R 
(EXIST A (ACTION A))))) NOW) 
A 
(KNOW MUTUAL 
(CAPABILITY USER A) NOW) 
=~ 
(BELIEVE SYSTEM 
(WANT USER 
(KNOW USER 
(AUGMENT S) 
FUTURE) 
NOW) 
NOW) 
Rule 2: Inference drawn from a user want referring to a state, given his/her ac- 
quaintance with the associated causal relation (pursuing a global goal) 
rain state (e.g., 'I want to have my mon- 
ey back'), the application of another 
rule almost identical to Rule 1 is at- 
tempted. When its successful applica- 
tion yields the association of a unique 
event, the required causal relation is 
established. Moreover, the user's fami- 
liarity with this relation must be deri- 
vable in order to follow the path indi- 
cated by Rule 2. Otherwise, a want of 
the user would be created whose con- 
tent is to find out about suitable means 
to achieve the desired state (as ex- 
pressed by Rule 3, leading to a system 
reaction like, e.g., 'you must dissolve 
your savings account'). 
It is very frequently the case that the 
satisfaction of a want cannot immedi- 
ately be achieved because the precision 
of its specification is insufficient. When 
the domain-specific problem solving 
component indicates a clue about what 
information would be helpful in this re- 
spect this triggers the creation of a sys- 
tem want to get acquainted with it. 
Whenever the user's uninformedness in 
a particular case is not yet proved, and 
this information falls into his/her com- 
petence area, control is passed to the ge- 
neration component to address a suit- 
able question to the user (as expressed 
in Rule 4). 
Provided with new information hopeful- 
ly obtained by the user's reply the sys- 
tem tries again to satisfy the (more pre- 
cisely specified) user want. This process 
is repeated until an adequate degree of 
specification is achieved at some stage. 
(KNOW MUTUAL 
(WANT USER 
(EXIST G 
(STATE G)) NOW) NOW) 
A 
(KNOW SYSTEM 
(EXIST R 
(AND (RULE R) 
(HAS-EFFECT R G) 
(HAS-PRECONDITION R 
(EXIST S (STATE S))) 
(HAS-ACTION R 
(EXIST A (ACTION A))))) NOW) 
A 
(-= (KNOW USER R NOW)) 
=~ 
(BELIEVE SYSTEM 
(WANT USER 
(KNOW USER 
R 
FUTURE) 
NOW) 
NOW) 
Rule 3: Inference drawn from a user want referring to a state, missing his/her ac- 
quaintance with the associated causal relation (pursuing a global goal) 
- 32 - 
(WANT SYSTEM 
(KNOW SYSTEM 
X FUTURE) NOW) 
A 
(BELIEVE SYSTEM 
(KNOW USER 
(RELATED X) 
NOW) NOW) 
A 
(-I (KNOW SYSTEM 
(-1 (KNOW USER 
x NOW)) NOW)) 
(ASK 
SYSTEM 
USER 
x) 
(KNOW MUTUAL 
(WANT SYSTEM 
(KNOW SYSTEM X 
FUTURE) 
NOW) NOW) 
A 
(KNOW MUTUAL 
(BELIEVE SYSTEM 
(KNOW USER 
(RELA TED X) 
NOW) 
NOW) NOW) 
Rule 4: Inference drawn from the user's (assumed) competence and a system 
want in this area (triggering a speech act) 
In the course of the dialog each utter- 
ance effects parts of the system's cur- 
rent model of the user (concerning as- 
sumptions or temporarily established 
knowledge). Therefore, these effects are 
checked in order to keep the data base 
consistent. Consider, for instance, a 
user want aiming at investing some 
money which, after a phase of para- 
meter assembling, has led to the system 
proposal 'I recommend you to buy 
bonds' apparently accomplishing the 
(substitued) goal of obtaining enough 
information to perform the envisioned 
action. Consequently, the state of the 
associated user want is subject to 
change which is expressed by Rule 5. 
Therefore, the mutual knowledge about 
the user want is modified (by closing 
the associated time-interval) and the 
the user's want is marked as being 'fin- 
ished' and added to the (new) mutual 
knowledge. 
However, this simplified treatment of 
the satisfaction of a want includes the 
restrictive assumptions that the accept- 
ance of the proposal is (implicitly) anti- 
cipated, and that modifications of a 
want or of a proposal are not manage- 
able. In a more elaborated version, the 
goal accomplishment has to be marked 
as provisory. If the user expresses 
his/her acceptance either explicitly or 
changes the topic (thus implicitly 
agreeing to the proposal), the appli- 
cation of Rule 5 is fully justified. 
Apart from the problem of the increas- 
ing complexity and the amount of ne- 
cessary additional rules, the prelimi- 
nary status of our solution has much to 
do with problems of interpreting the AUGMENT-predicate 
which appears in 
the representation of a communicative 
goal according to the derivation by Rule 
2: The system is satisfied by finding any 
additional information augmenting the 
user's knowledge, but it is not aware of 
the requirement that the information 
must be a suitable supplement (which is 
recognizable by the user's confirmation 
only). 
(KNOW MUTUAL 
(WANT USER (MEETS TI NOW) 
X NOW) A 
(EXIST TI (KNOW MUTUAL 
(AND (TIME-INTERVAL TI) =:~ (WANT USER 
(DURING TI NOW)))) X 
A PAST) 
(KNOW MUTUAL NOW) 
x NOW) 
Rule 5: Inference drawn from a (mutually known) user want which the user 
knows to be accomplished (pursuing consistency maintenance) 
- 33 - 
FUTURE RESEARCH 
The method described in this paper is 
fully implemented and integrated in 
the complete NL system WISBER. A re- 
latively small set of rules has proved 
sufficient to guide basic consultation di- 
alogs. Currently we are extending the 
set of dialog control rules to perform 
more complex dialogs. Our special in- 
terest lies on clarification dialogs to 
handle misconceptions and inconsisten- 
cies. The first steps towards handling 
inconsistent user goals will be an expli- 
cit representation of the interrelation- 
ships holding between propositional at- 
titudes, e.g., goals being simultaneous 
or competing, or one goal being a re- 
finement of another goal. A major ques- 
tion will be specifying the operations 
necessary to recognize those interrela- 
tionships working on the semantic re- 
presentation of the propositional con- 
tents. As our set of rules grows, a more 
sophisticated control mechanism will 
become necessary, structuring the deri- 
vation process and employing both for- 
ward and backward reasoning. 
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