A FLEXIBLE APPROACH TO COOPERATIVE RESPONSE GENERATION 
IN INFORMATION-SEEKING DIALOGUES 
Liliana Ardissono, Alessandro Lombardo, Dario Sestero 
Dipartimento di Informatica - Universita' di Torino 
C.so Svizzera 185 - 10149 - Torino - Italy 
E-Mail: liliana@di.unito.it 
Abstract 
This paper presents a cooperative consultation system 
on a restricted domain. The system builds hypotheses 
on the user's plan and avoids misunderstandings (with 
consequent repair dialogues) through clarification 
dialogues in case of ambiguity. The role played by 
constraints in the generation of the answer is charac- 
terized in order to limit the cases of ambiguities re- 
quiring a clarification dialogue. The answers of the 
system are generated at different levels of detail, ac- 
cording to the user's competence in the domain. 
INTRODUCTION 
This paper presents a plan-based consultation system 
for getting information on how to achieve a goal in a 
restricted domain, l The main purpose of the system is 
to recognize the user's plans and goals to build coop- 
erative answers in a flexible way \[Allen, 83\], 
\[Carberry, 90\]. The system is composed of two parts: 
hypotheses construction and response generation. 
The construction of hypotheses is based on Context 
Models (CMs) \[Carberry, 90\]. Carberry uses default 
inferences \[Carberry, 90b\] to select a single hypothe- 
sis for building the final answer of the system and, in 
case the choice is incorrect, a repair dialogue is 
started. Instead, in our system, we consider all plau- 
sible hypotheses and if the ambiguity among them is 
relevant for the generation of the response, we try to 
solve it by starting a clarification dialogue. According 
to \[van Beek and Cohen, 91\], clarification dialogues 
are simpler for the user than repair ones, because they 
only involve yeshlo questions on the selected ambigu- 
ous plans. Furthermore, repair dialogues generally re- 
quire a stronger participation of the user. Finally, if 
the misunderstanding is not discovered, the system 
delivers information that is not proper to the user's 
case. For these reasons, it is preferable to solve the 
runbiguity a priori, by asking the user information on 
his intentions. In van Beek and Cohen's approach 
cl,'wification dialogues are started, even in case the 
answers associated with the plausible hypotheses are 
distinguished by features that could dhectly be 
managed in the answer. We avoid this by identifying 
the constraints relevant for a clarification dialogue 
and those which can be mentioned in the answer. In 
this way, the friendliness of the system is improved 
lThe system is concerned with information about a CS 
Deparunent. 
and the number and the length of the clarification dia- 
logues are reduced. 
In the perspective of generating flexible 
cooperative answers, it is important to differentiate 
their detail level by adapting them to the user's 
competence in the domain. In our work, we want to 
study how to embed information obtained from a user 
model component in the system. As a first step in this 
direction, we introduce a preliminary classification of 
users in three standard levels of competence 
corresponding to the major users' prototypes the 
system is devoted to. Then, in order to produce 
differentiated answers, the hypotheses are expanded 
according to the user's competence level. 
The knowledge about actions and plans is stored in 
a plan library structured on the basis of two main hier- 
archies: the Decomposition Hierarchy (DH) and the 
Generalization Hierarchy (GH) \[Kautz and Allen, 86\]. 
The first one describes the plans associated with the 
actions and is used for explaining how to execute a 
complex action. The second one expresses the 
relation among genera/and specific actions (the major 
specificity is due to additional restrictions on 
parameters). It supports an inheritance mechanism 
and a top-down form of clarification dialogue. 
THE ALGORITHM 
The algorithm consists of two parts: a hypotheses 
construction and a response generation phase. 
• 111 the hypotheses construction phase the following 
steps are repeated for each sentence of the user: 
1- Action identification: on the basis of the user's ut- 
terance, a set of candidate actions is selected. 
2- Focusing: CMs built after the analysis of the pre- 
vious sentences are analyzed to find a connection 
with any candidate action identified in step 1 and, for 
each established connection, a new CM is built. (At 
the beginning of the dialogue, from each candidate 
action a CM is created). 
3- Upward expansion along the DH: each CM is ex- 
panded (when possible) by appending it to the more 
complex action having the root of the CM itself in its 
decomposition. 111 this way we get a higher lever de- 
scription of the action that the user wants to pursue. 
4- Weighted expansion along the DH: for each CM, 
its actions are repeatedly decomposed in more ele- 
mentary ones, until all the steps of the CM are suffi- 
ciently simple for the user's competence level in the 
domain. In this way, the information necessary to 
generate an answer suitable to the user is collected. 
274 
5- Weighted expansion backward through enable- 
ment links: each CM is expanded in order to include 
the actions necessary for satisfying the preconditions 
which the user is supposed not to be able to plata by 
himself (according to his competence level). 
• In the response generation phase, the ambiguity 
among the hypotheses is evaluated. If it is relevant, a 
top-down clarification dialogue guided by the GH is 
started up. Finally, the answer is extracted from the 
CMs selected through the clarification dialogue. 
THE REPRESENTATION OF GOALS, 
PLANS AND ACTIONS 
The basic elements of representation of the domain 
knowledge are goals and actions. Actions can be ele- 
mentary or complex and in the second case one or 
more plans (decompositions) can be associated with 
them. All these plans share the same main effect. 
Each action is characterized by the preconditions, 
constraints, restrictions on the action parmneters, ef- 
fects, associated plans mid position in the GH. The re- 
strictions specify die relationship among the par,'une- 
ters of the main action and diose of the action sub- 
steps. During the response generation phase, if the 
value of some parameters is still unknown, their refer- 
ent can be substituted in die answer by a linguistic de- 
scription extracted from the restrictions, so avoiding 
further questions to the user. For example, if the user 
says that he wants to talk to the advisor for a course 
plan, but he does not specify which (so it is not possi- 
ble to determine the name of the advisor), still the 
system may suggest: "talk with the advisor for the 
course plan you are interested in". 
The GH supports an inheritance mechanism in the 
plan library. Moreover, it allows to describe the de- 
composition of an action by means of a more abstract 
specification of some of its substeps when no specific 
information is available. For exainple, a step of die 
action of getting information on a course plan is to 
talk with the curriculum advisor, that can be 
• specialized in different ways according to the topic of 
the conversation (talking by phone and talking face to 
face). If in a specific situation the actual topic is un- 
known, it is not possible to select one possibility. So, 
the more general action of talking is considered. 
In order to support the two phases of weighted ex- 
pansion, information about the difficulty degree of the 
actions is embedded in the plan library by labelling 
them with a weight that is a requested competence 
threshold (if the user is expert for an action, it is taken 
as elementary for him, otherwise its steps must be 
specified). Preconditions are labelled in an analogous 
way, so as to specify which users know how to plan 
them by themselves and which need an exph'mation. 
THE CONSTRUCTION OF THE 
HYPOTHESES 
In the action identification phase a set of actions is 
selected from the plan library, each of them possibly 
repl~esenting the aspect of the task on which the user's 
attention is currently focused. The action identifica- 
tion is accomplished by means of partially ordered 
rules (a rule is more specific than another one if it im- 
poses greater constraints on the structure of the log- 
ical form of the user's utterance). Restrictions on the 
pmameters of conditions and actions are used to select 
the most specific action from the plan library that is 
supported by the user's utterance. 
In the focusing phase the set of CMs produced by 
the analysis of the previous sentences and the set of 
candidate actions selected in the action identification 
phase are considered. A new set of CMs is built, all of 
which are obtained by expanding one of the given 
CMs so as to include a candidate action. CMs for 
which no links with the candidate actions have been 
found are discarded. The expansion of the CMs is 
similar to that of Carberry. However, because of our 
approach to the response generation, when a focusing 
rule fires, the expansion is tried backward through the 
enablement links and along the DH and the GH, so to 
find all the connections with the candidate actions 
without preferring any possibility. If a heuristic rule 
suggests more than one connection, a new CM is 
generated for each one. 
After the focusing phase, a further expansion up 
through tim DH is provided for each CM whose root 
is part of only one higher-level plan. 
In the weighted expansion along the DH, for each 
CM, every action to be included in the answer is ex- 
panded with its decomposition if it is not elementary 
for the user's competence level. Actually, only actions 
with a single decomposition are expanded 2 The ex- 
pansion is performed until the actions to be 
mentioned in the answer are not decomposable or 
they suit the user's competence level. 
In the weighted expansion backward through en- 
ablement links, for each CM, preconditions whose 
planning is not immediate for the user are expanded 
by attaching to their CMs the actions having them as 
effects. When a precondition to be expanded is of the 
form "Know(IS, x)" and the system knows the value 
of "x", it includes such information in the response; 
so, the expansion is avoided. While in the previous 
phase the expansion is performed recursively, here it 
is not, because expanding along the enablement 
chain extends the CM far from the current focus. 
2 In the last two expansion phases we did not want to 
extend the set of alternative hypotheses. In particular, in the 
weighted expansion along the DH, the choice does not 
reduce the generality of our approach because this kind of 
ambiguity lies at a more detailed level than that of the user's 
expressions. Anyway, the specificity of the actions 
mentioned in the answer can be considered a matter of 
txade-off between the need of being cooperative and the risk 
of generating too complex answers. 
275 
THE RESPONSE GENERATION 
In the relevance evaluation phase, the ambiguity 
among candidate hypotheses filtered out in the focus- 
ing phase is considered. The notion of relevance de- 
fined by van Beek and Cohen is principally based on 
the conditions (corresponding to our constraints) as- 
sociated with the selected plans. We further specify 
this notion in two ways, in order to avoid a clarifica- 
tion dialogue when it is not necessary because a more 
structured answer is sufficient for dealing with the 
ambiguity. First we classify constraints into three cat- 
egories: those with a value known to the system, that 
are the only to be used in order to evaluate the rele- 
vance of ambiguity; those that involve information 
peculiar to the user (e.g. if he is a studen0, that can be 
mentioned in the answer as assumptions for its valid- 
ity; finally, those with a value unknown to both the 
user and the system, but that the user can verify by 
himself (e.g. the availability of books in the library). 
Also constraints of the last category should be in- 
cluded in the answer providing a recormnendation to 
check them. Second, clarification dialogues can be 
avoided even when the ambiguity is relevant, but all 
the selected hypotheses are invalidated by some false 
constraints whose truth value does not cllange in the 
considered situation; hence, a definitive negative an- 
swer can be provided. Clarification dialogues are or- 
ganized in a top-down way, along the GH. 
In our approach, answers should include not only 
information about the involved constraints, but also 
about the specific description of how the user should 
accomplish his task. For this reason, we consider a 
clarification dialogue based on constraints as a first 
step towards a more complex one, that takes into ac- 
count the ambiguity among sequences of steps as 
well. In the future work, we are going to complete the 
answer generation phase by developing tiffs part, as 
well as the proper answer generation part. 
AN EXAMPLE 
Let us suppose that a CM produced in the previous 
analysis is composed by tile action Get-info-on- 
course-plan (one of whose steps is the Talk-prof ac- 
tion) and the user asks if Prof. Smith is in his office. 
The action identification phase selects the Talk-by- 
phone and Meet actions, that share tile constraint that 
the professor is ill his office. Since the two actions are 
decompositions of tile Talk-prof action, the focusing 
phase produces two CMs from the previous one. If 
tile user is expert on the domain, no further expansion 
of the CMs is needed for the generation of the answer, 
that could be "Yes, he is; you can phone him to num- 
ber 64 or meet him in office 42". On tile other hand, if 
the user has a lower degree of competence, tile steps 
difficult for him are expanded. For example, the Talk- 
by-phone action is detailed by specifying: "To phone 
him go to the internal phone in tile entrance". In order 
to show one of the cases that differentiate van Beek 
and Cohen's approach from ours, suppose to add to 
the action Meet the constraint Is-meeting-time and 
that the user asks his question when the professor is 
not in the office and it is not his meeting time. In this 
case, the false constraint Is-meeting-time causes the 
ambiguity to be relevant for van Beek and Cohen; on 
the other hand, our system provides the user with a 
unique negative answer, so avoiding any clarification 
dialogue. 
CONCLUSIONS 
The paper presented a plan-based consultation sys- 
tem whose main purpose is to generate cooperative 
answers on the basis of recognition of the user's plans 
and goals. In the system, repair dialogues due to mis- 
understandings of the user's intentions are prevented 
through a possible clarification dialogue. 
In order to enhance the flexibility of the system, 
different detail levels have been provided for the an- 
swers, according to the competence of the various 
users. This has been done by specifying the difficulty 
degree of the various components of the plan library 
and by expanding the CMs until the information pro- 
vided for the generation of an answer is suitable for 
the user. Van Beek and Cohen' notion of the 
relevance of ambiguity has been refined on the basis 
of the characteristics of the constraints present in the 
plans. 
In the future work, we are going to refine the notion 
of relevance of ambiguity in order to deal with the 
presence of different sequences of actions in the pos- 
sible answers. Finally we are going to complete the 
proper answer generation. 
A C KNOWLEDGEMENTS 
The authors are indebted to Leonardo Lesmo for 
many useful discussions on the topic presented in the 
paper. The authors are also grateful to the four 
anonimous referees for their useful comments. 
This research has been supported by CNR in the 
project Pianificazione Automatica. 

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