PLANNING TEXT FOR ADVISORY DIALOGUES" 
Johanna D. Moore 
UCLA Department of Computer Science 
and 
USC/Information Sciences Institute 
4676 Admiralty Way 
Marina del Key, CA 90292-6695, USA 
C~cile L. Paris 
USC/information Sciences Institute 
4676 Admiralty Way 
Marina del Key, CA 90292-6695, USA 
ABSTRACT 
Explanation is an interactive process re- 
quiring a dialogue between advice-giver and 
advice-seeker. In this paper, we argue that 
in order to participate in a dialogue with its 
users, a generation system must be capable of 
reasoning about its own utterances and there- 
fore must maintain a rich representation of 
the responses it produces. We present a text 
planner that constructs a detailed text plan, 
containing the intentional, attentional, and 
.,,e~,~nc~ ~tructures of the text it generates. 
INTRODUCTION 
Providing explanations in an advisory situa- 
tion is a highly interactive process, requiring 
a dialogue between advice-giver and advice- 
seeker (Pollack eta/., 1982). Participating in 
a dialogue requires the ability to reason about 
previous responses, e.g., to interpret the user's 
follow-up questions in the context of the on- 
going conversation and to determine how to 
clarify a response when necessary. To pro- 
vide these capabilities, an explanation facility 
must understand what it was trying to convey 
and how that information was conveyed, i.e., 
the intentional structure behind the explana- 
tion, including thegoal of the explanation as a 
whole, the subgoal(s)of individual parts of the 
explanation, and the rhetorical means used to 
achieve them. 
Researchers in natural language under. standing 
have recognized the need for such 
information. In their work on discourse anal- 
ysis, Grosz and Sidner (1986) argue that it is 
necessary to represent the intentional struc- 
ture, the attentional structure (knowledge 
about which aspects of a dialogue are in focus 
at each point), and the linguistic structure of 
"The research described in this paper was sup- 
ported by the Defense Advanced Research Projects 
Agency (DARPA) under a NASA Ames cooperative 
agreement number NCC 2-520. The authors would 
like to thank William Swartout for comments on ear- 
lier versions of this paper. 
203 
the discourse. In contrast, most text gener- 
ation systems (with the notable exception of 
KAMP (Appelt, 1985)) have used only rhetor- 
ical and attentional information to produce 
coherent text (McKeown, 1985, McCoy, 1985, 
Paris, 1988b), omitting intentional informa- 
tion, or conflating intentional and rhetorical 
information (Hovy, 1988b). No text gener- 
ation system records or reasons about the 
rhetorical, the attentional, as well as the in- 
tentional structures of the texts it produces. 
In this paper, we argue that to success- 
fully participate in an explanation dialogue, 
a generation system must maintain the kinds 
of information outlined by Grosz and Sidner 
as well as an explicit representation of the 
rhetorical structure of the texts it generates. 
We present a text planner that builds a de- tailed 
text plan, containing the intentional, 
attentional, and rhetorical structures of the 
responses it produces. The main focus of 
this paper is the plan language and the plan 
structure built by our system. Examples of 
how this structure is used in answering follow- 
up questions appear in (Moore and Swartout, 
1989). 
WHY A DETAILED TEXT PLAN? 
In order to handle follow-up questions that 
may arise if the user does not fully understand 
a response given by the system, a generation 
facility must be able to determine what por- 
tion of the text failed to achieve its purpose. If 
the generation system only knows the top-level 
discourse goal that was being achieved by the 
text (e.g., persuade the hearer to perform an 
action), and not what effect the individual 
parts of the text were intended to have on the 
hearer and how they fit together to achieve 
this top-level goal, its only recourse is to use a 
different strategy to achieve the top-level goal. 
It is not able to re-explain or clarify any part 
of the explanation. There is thus a need for 
a text plan to contain a specification of the 
intended effect of individual parts of the text 
on the hearer and how the parts relate to one 
another. We have developed a text planner 
that records the following information about 
the responses it produces: 
• the information that Grosz and Sidner 
(1986) have presented as the basics of a 
discourse structure: 
- intentional structure: a represen- 
tation of the effect each part of 
the text is intended to have on the 
hearer and how the complete text 
achieves the overall discourse pur- 
pose (e.g., describe entity, persuade 
hearer to perform an action). 
- attentional structure: information / 
about which objects, properties and 
events are salient at each point 
in the discourse. User's follow- 
up questions are often ambiguous. 
Information about the attentional 
state of the discourse can be used 
to disambiguate them (cf. (Moore 
and Swartout, 1989)). 
• in addition, for generation we require the 
following: 
- rhetorical structure: an agent must 
understand how each part of the 
text relates rhetorically to the oth- 
ers. This is necessary for linguis- 
tic reasons (e.g., to generate the 
appropriate clausal connectives in 
multi-sentential responses) and for 
responding to requests for elabora- 
tion/clarification. 
• assumption information: ad'vice- 
giving systems must take knowl- 
edge about their users into account. 
However, since we cannot rely on 
having complete user models, these 
systems may have to make assump- 
tions about the hearer in order to 
use a particular explanation strat- 
egy. Whenever such assumptions 
are made, they must be recorded. 
The next sections describe this new text plan- 
ner and show how it records the information 
needed to engage in a dialogue. Finally, a brief 
comparison with other approaches to text gen- 
eration is presented. 
TEXT PLANNER 
The text planner has been developed as part 
of an explanation facility for an expert sys- 
tern built using the Explainable Expert Sys- 
tems (EES) framework (Swartout and Smo- 
liar, 1987). The text planner has been used 
in two applications. In this paper, we draw 
our examples from one of them, the Program 
Enhancement Advisor (PEA) (Neches et al., 
1985). PEA is an advice-giving system in- 
tended to aid users in improving their Com- 
mon Lisp programs by recommending trans- 
formations that enhance the user's code. 1 The 
user supplies PEA with a program and in- 
dicates which characteristics of the program 
should be enhanced (any combination of read- 
ability, maintainability, and efficiency). PEA 
then recommends transformations. After each 
recommendation is made, the user is free to 
ask questions about the recommendation. 
We have implemented a top-down hier- 
archical expansion planner (d la Sacerdoti 
(1975)) that plans utterances to achieve dis- 
course goals, building (and recording) the in- 
tentional, attentional, and rhetorical struc- 
ture of the generated text. In addition, since 
the expert system explanation facility is in- 
tended to be used by many different users, 
the text planner takes knowledge about the 
user into account. In our system, the user 
model contains the user's domain goals and 
the knowledge he is assumed to have about 
the domain. 
THE PLAN LANGUAGE 
In our plan language, intentional goals are 
represented in terms of the effects the speaker 
intends his utterance to have on the hearer. 
Following Hovy (1988a), we use the terminol- 
ogy for expressing beliefs developed by Cohen 
and Levesque (1985) in their theory of ratio- 
nal interaction, but have found the need to 
extend the terminology to represent the types 
of intentional goals necessary for the kinds 
of responses desired in an advisory setting. 
Although Cohen and Levesque have subse- 
quently retracted some aspects of their theory 
of rational interaction (Cohen and Levesque, 
1987), the utility of their notation for our pur- 
poses remains unaffected, as argued in (Hovy, 
1989). 2 
a PEA recommends transformations that improve 
the 'style' of the user's code. It does not attempt to 
understand the content of the user's program. 
2Space limitations prohibit an exposition of their 
terminology in this paper. We provide English para- 
phrases where necessary for clarity. (BR8 S II x) 
should be read as 'the speaker believes the speaker 
and hearer mutually believe x.' 
204 
EFFECT: (PERSUADE S H (GOAL H Eventually(DONE H ?act))) 
CONSTRAINTS: (AND (GOAL S ?domain-goal) 
(STEP ?act ?domain-goal) 
(BMB S H (GOAL H ?domaln-goal))) 
NUCLEUS: (FOR.ALL ?domain-goal 
(MOTIVATION ?act ?domain-goal)) 
SATELLITES: nil 
Figure 1: Plan Operator for Persuading the Hearer to Do An Act 
EFFECT: (MOTIVATION ?act ?domain-goal) 
CONSTRAINTS: (AND (GOAL S ?domain-goal) 
(STEP ?act ?domain-goal) 
(BMB S H (GOAL H ?domain-goal)) 
(ISA ?act REPLACE)) 
NUCLEUS: ((SETQ ?replacee (FILLER-OF OBJECT ?act)) 
(SETQ ?replacer (FILLER-OF GENERALIZED-MEANS ?act)) 
(BMB S H (DIFFERENCES ?repLacee ?repLacer ?domain-goal)) ) 
SATELLITES: nll 
Figure 2: Plan Operator for Motivating a Replacement by Describing Differences between Replacer 
and Replacee 
Rhetorical structure is represented in 
terms of the rhetorical relations defined in 
Rhetorical Structure Theory (RST) (Mann 
and Thompson, 1987), a descriptive theory 
characterizing text structure in terms of the 
relations that hold between parts of a text 
(e.g., CONTRAST, MOTIVATION). The defini- 
tion of each RST relation includes constraints 
on the two entities being related as well as 
constraints on their combination, and a spec- 
ification of the effect which the speaker is 
attempting to achieve on the hearer's be- 
lids. Although other researchers have cate- 
gorized typical intersentential relations (e.g., 
(Grimes, 1975, Hobbs, 1978)), the set of rela- 
tions proposed by RST is the most complete 
and the theory sufficiently detailed to be eas- 
ily adapted for use in generation. 
In our plan language, each plan operator 
consists of: 
an effect: a characterization of what 
goai(s) this operator can be used to 
achieve. An effect may be an in- 
tentional goal, such as persuade the 
hearer to do an ac~ionorarhetorical 
relation, such as provide motivation 
for an action. 
a constraint list: a list of conditions that 
must be true before the operator can be 
applied. Constraints may refer to facts 
in the system's knowledge base or in the 
user model. 
• a nucleus: the main topic to be ex- 
pressed. The nucleus is either a prim- 
itive operator (i.e., speech acts such as 
inform, recommend and ask) or a goal 
intentional or rhetorical) which must be 
ther expanded. All operators must 
contain a nucleus. 
• satellites: subgoal(s)that express addi- 
tional information which may be needed 
to achieve the effect of the operator. 
When present, satellites may be specified 
as required or optional. 
Examples of our plan operators are shown 
in Figures 1 and 2. The operator shown in 
Figure 1 can be used if the speaker (S) intends 
to persuade the hearer (H) to intend to do 
some act. This plan operator states that if an 
act is a step in achieving some domain goal(s) 
that the hearer shares, one way to persuade 
the hearer to do the act is to motivate the act 
in terms of those domain goals. Note that this 
plan operator takes into account not only the 
system's knowledge of itself, but also the sys- 
tem's knowledge about the user's goals, as em- 
bodied in a user model. If any domain goals 
that satisfy the constraints are found, this op- 
erator will cause the planner to post one or 
more MOTIVATION subgoals. This plan opera- 
tor thus indicates that one way to achieve the 
intentional goal of persuading the hearer to 
perform an action is by using the rhetorical 
means MOTIVATION. 
205 
EFFECT: (BMB S H ?x) 
CONSTRAINTS: nil 
NUCLEUS: (INFORM S H ?x) 
SATELLITES: (((PERSUADE S H 7x) *optional*)) 
Figure 3: Plan Operator for Achieving Mutual Belief of a Proposition 
SYSTEM 
USER 
SYSTEM 
" USER 
SYSTEM 
What characteristics of the program would you like to enhance? 
Maintainability. 
You should replace (setq x I) with (serf x I). Serf can be used to assign a 
value to any generalized-variable. Serq can only be used to assign a value to a 
simple-variable. A generalized-variable is a storage location that can be named by 
any accessor function. 
What is a generalized variable? 
For example, the car and cdr of a cons are generalized-variables, named by the 
accessor functions car and cdr. Other examples are an element of an array or a 
component of a structure. 
Figure 4: Sample Dialogue 
\[11 
P-\] 
\[31 
\[4\] 
\[51 
Plans that achieve intentional goals and 
those that achieve rhetorical relations are dis- 
tinguished for two reasons: (1) so that the 
completed plan structure contains both the in- 
tentional goals of the speaker and the rhetor- 
ical means used to achieve them; (2) because 
there are many different rhetorical strategies 
for achieving any given intentional goal. For 
example, the system has several plan opera- 
tors for achieving the intentional goal of de- 
scribing a concept. It may describe a concept 
by stating its class membership and describ- 
ing its attributes and its parts, by drawing 
an analogy to a similar concept, or by giving 
examples of the concept. There may also be 
many different plan operators for achieving 
a particular rhetorical strategy. (The plan- 
ner employs selection heuristics for choosing 
among applicable operators in a given situa- 
tion (Moore and Swartout, 1989).) 
Our plan language allows both general 
and specific plans to be represented. For ex- 
ample, Figure 2 shows a plan operator for 
achieving the rhetorical relation MOTIVATION. 
This is a very specific operator that can be 
used only when the act to be motivated is a 
replacement (e.g., replace sezq with sezf). 
In this case, one strategy for motivating the 
act is to compare the object being replaced 
and the object that replaces it with respect 
to the domain goal being achieved. On the 
other hand, the operator shown in Figure 3 
is general and can be used to achieve mu- 
tual belief of any assertion by first inform- 
ing the hearer of the assertion and then, op- 
tionaUy, by persuading him of that fact. Be- 
cause we allow very general operators as well 
as very specific ones, we can include both 
domain-independent and domain-dependent 
strategies. 
A DETAILED EXAMPLE 
Consider the sample dialogue with our sys- 
tem shown in Figure 4, in which the user in- 
dicates that he wishes to enhance the main- 
tainability of his program. While enhanc- 
ing maintainability, the system recommends 
that the user perform the act replace-I, 
namely 'replace setq with serf', and thus 
posts the intentional goal (BMB S H (GOAL 
H Evenzually(DONE H replace-I))). This 
discourse goal says that the speaker would like 
to achieve the state where the speaker believes 
that the hearer and speaker mutually believe 
that it is a goal of the hearer that the replace- 
ment eventually be done by the hearer. 
The planner then identifies all the opera- 
tors whose effect field matches the discourse 
goal to be achieved. For each operator found, 
the planner checks to see if all of its con- 
straints are satisfied. In doing so, the text 
planner attempts to find variable bindings in 
the expert system's knowledge base or the 
user model that satisfy all the constraints in 
206 
EFFECT: (BMB S H (GOAL H Eventually(DONE H ?act))) 
CONSTRAINTS: none 
NUCLEUS: (RECOMMEND S H ?act) 
SATELLITES: (((BMB S H (COMPETENT H (DONE H ?act))) *optional*) 
((PERSUADE S H (GOAL H Eventually(DONE H 7act))) *optional*) ) 
Figure 5: High-level Plan Operator for Recommending an Act 
apply-SETQ-t o-SETF-~rans formal; ion 
apply-lo cal-1;ransf ormat ions-whos e-rhs-us e-is-mor e-general-1:han-lhs-us • 
apply-local-1;rans f orma1~ions-thal;-enhance-mainl;ainability 
apply-1~ransforma¢ ions-1~hal;-enhanc e-mainl; ainabili~y 
enhanc e-mainl; ainabili1: y 
enhance-program 
Figure 6: System goals leading to replace setq wil;h sel;f 
the constraint list. Those operators whose 
constraints are satisfied become candidates for 
achieving the goal, and the planner chooses 
one based on: the user model, the dialogue 
history, the specificity of the plan operator, 
and whether or not assumptions about the 
user's beliefs must be made in order to satisfy 
the operator's constraints. 
Continuing the example, the current dis- 
course goal is to achieve the state where 
it is mutually believed by the speaker and 
hearer that the hearer has the goal of even- 
tually executing the replacement. This dis- 
course goal can be achieved by the plan op- 
erator in Figure 5. This operator has no 
constraints. Assume it is chosen in this 
case. The nucleus is expanded first, 3 causing 
(RECOMMEND S H replace-l) to be posted as 
a subgoal. RECOMMEND is a primitive operator, 
and so expansion of this branch of the plan is 
complete. 4 
Next, the planner must expand the satel- 
lites. Since both satellites are optional in this 
case, the planner must decide which, if any, 
are to be posted as subgoals. In this example, 
the first satellite will not be expanded because 
the user model indicates that the user is ca- 
31n some cases, such as a satellite posting the rhetorical relation background, the satellite is ex- 
panded first. 
+At this point, (RECOMMEND S H replace-l) must be translated into a form appropriate as input.to the 
realization component, the Penman system (Mann, 
1983, Kasper, 1989). Based on the type of speech act, its arguments, and the context in which it occurs, the 
planner builds the appropriate structure. Bateman and Paxis (1989) have begun to investigate the prob- 
lem of phrasing utterances for different types of users. 
pable of performing replacement acts. The 
second satellite is expanded, s posting the in- 
tentional subgoal to persuade the user to per- 
form the replacement. A plan operator for 
acldeving this goal using the rhetorical rela- 
tion MOTIVATION was shown in Figure i. 
When attempting to satisfy the con- 
straints of the operator in Figure 1, the 
system first checks the constraints (GOAL 
S ?domain-goal) and (STEP replace-1 
?domain-goal). These constraints state that, 
in order to use this operator, the system must 
find an expert system goal, ?domain-goal, 
that replace-I is a step in achieving. 
This results in several possible bindings 
for the variable ?domain-goal. In this case, 
the applicable system goals, listed in order 
from most specific to the top-level goal of the 
system, are shown in Figure 6. 
The last constraint of this plan opera- 
tor, (BMB S H (GOAL H ?domain-goal)), is 
a constraint on the user model stating that the 
speaker and hearer should mutu~IIy believe 
that ?domain-goal is a goal of the hearer. 
Not all of the bindings found so far will sat- 
isfy this constraint. Those which do not will 
not be rejected immediately, however, as we 
do not assume that the user model is com- 
plete. Instead, they will be noted as possible 
bindings, and each will be marked to indicate 
that, if this binding is used, an assumption 
is being made, namely that the binding of 
Sin other situations, the system could choose not to expand this satellite and await feedback from the 
user instead (Moore and Swartout, 1989). 
207 
(BMB S H (GOAL H Eventually (DONE H replace-I))) NI 
(MOTIVATION replace1 enhance-maintainability) 
(RECOMMEND S H replace-I) (PERSUADE S H (GOAL H Eventually (DONE H replace-I))) NI 
(MOTIVATION replace-1 enhance-maintainability) .I 
(BMB S H (DIFFERENCES setq serf enhance-maintainability)) NI 
N (BMB S H (DIFFERENCE setq serf use)) S 
(INFORM S H (IDENTITY (VALUE-OF use serf) S assign-value.to-generalized-variableJJ (BMR S H (KNOW H generalized-variable)) 
(CONTRAST (IDENTITY (VALUE-OF use setq))) N 
N I (ELABORATION general zed-variable) 
(INFORM S H (IDENTITY (VALU E-OF use setq) ~ ~ S 
assign-value-to-sim pie-variable)) ~ , 
(INFORM S H (CLASS-ASCRIPTION (ELABORATION-OBJECT-ATTRIBUTE 
generalized-variable storage-location)) generalized-variable named-by) 
repla(el = replm:eSETQwithSETF N \[ N • Nucleus 
S = Satellite (INFORM S H (IDENTrI"Y 
(VALUE-OF named-by accessor-function ))) 
Figure 7: Completed Text Plan for Recommending Replace SETQ with SETF 
?domain-goal is assumed to be a goal of the 
user. 
In this example, since the user is using 
the system to enhance a program and has in- 
dicated that he wishes to enhance the main- 
tainability of the program, the system infers 
the user shares the top-level goal of the system 
(enhance-program), as well as the more spe- 
cific goal enhance-mainZainabilizy. There- 
fore, these are the two goals that satisfy the 
constraints of the operator shown in Figure I. 
The text planner prefers choosing binding 
environments that require no assumptions to 
be made. In addition, in order to avoid ex- 
plaining parts of the reasoning chain that the 
user is familiar with, the most specific goal is 
chosen. The plan operator is thus instanti- 
ated with enhance-mainzainability as the 
binding for the variable ?domain-goal. The 
selected plan operator is recorded as such, and 
all other candidate operators are recorded as 
untried alternatives. 
The nucleus of the chosen plan op- 
erator is now posted, resulting in the 
subgoal (MOTIVATION replace-1 enhance- 
mainZainability). The plan operator cho- 
sen for achieving this goal is the one that 
208 
was shown in Figure 2. This operator mo- 
tivates the replacement by describing differ- 
ences between the object being replaced and 
the object replacing it. Although there are 
many differences between sezq and serf, 
only the differences relevant to the domain 
goal at hand (enhance-mainzainabilizy) 
should be expressed. The relevant differ- 
ences are determined in the following way. 
From the expert system's problem-solving 
knowledge, the planner determines what roles 
eezq and eezf play in achieving the goal 
enhance-maintainabilizy. In this case, the 
system is enhancing maintainability by ap- 
plying transformations that replace a specific 
construct with one that has a more general 
usage. SeZq has a more specific usage than 
sezf, and thus the comparison between sezq 
and sezf should be based on the generality of 
their usage. 
Finally, since the term generalized- 
variable has been introduced, and the 
user model indicates that the user does 
not know this term, an intentional goal 
to define it is posted: (BMB S H (KNOW 
H generalized-variable)). This goal is 
achieved with a plan operator that describes 
concepts by stating their class membership 
and describing their attributes. Once com- 
pleted, the text plan is recorded in the dia- 
logue history. The completed text plan for 
response (3) of the sample dialogue is shown 
in Figure 7. 
ADVANTAGES 
As illustrated in Figure 7, a text plan pro- 
duced by our planner provides a detailed rep- 
resentation of the text generated by the sys- 
tem, indicating which purposes different parts 
of the text serve, the rhetorical means used 
to achieve them, and how parts of the plan 
are related to each other. The text plan also 
contains the assumptions that were made dur- 
ing planning. This text plan thus contains 
both the intentional structure and the rhetor- 
ical structure of the generated text. From 
this tree, the dominance and saris/action- 
precedence relationships as defined by Grosz 
and Sidner can be inferred. Intentional goals 
higher up in the tree dominate those lower 
down and a left to right traversal of the 
tree provides satisfaction-precedence ordering. 
The attentional structure of the generated 
text can also be derived from the text plan. 
The text plan records the order in which top- 
ics appear in the explanation. The global vari- 
able *local-contezt ~ always points to the plan 
node that is currently in focus, and previously 
focused topics can be derived by an upward 
traversal of the plan tree. 
The information contained in the text 
plan is necessary for a generation system to be 
able to answer follow-up questions in context. 
Follow-up questions are likely to refer to the 
previously generated text, and, in addition, 
they often refer to part of the generated text, 
as opposed to the whole text. Without an ex- 
plicit representation of the intentional struc- 
ture of the text, a system cannot recognize 
that a follow-up question refers to a portion of 
the text already generated. Even if the system 
realizes that the follow-up question refers back 
to the original text, it cannot plan a text to 
clarify a part of the text, as it no longer knows 
what were the intentions behind various pieces 
of the text. 
Consider again the dialogue in Figure 4. 
When the user asks 'What is a gener- 
alized variable?' (utterance (4) in Fig- 
ure 4), the query analyzer interprets this ques- 
tion and posts the goal: (BMB S H (KNOW H 
generalized-variable) ). At this point, the 
explainer must recognize that this discourse 
goal was attempted and not achieved by the 
209 
last sentence of the previous explanation. 6 
Failure to do so would lead to simply repeat- 
ing the description of a generalized variable 
that the user did not understand. By exam- 
ining the text plan of the previous explanation 
recorded in the dialogue history, the explainer 
is able to determine whether the current goal 
(resulting from the follow-up question) is a 
goal that was attempted and failed, as it is 
in this case. This time, when attempting to 
achieve the goal, the planner must select an al- 
ternative strategy. Moore (1989b) has devised 
recovery heuristics for selecting an alternative 
strategy when responding to such follow-up 
questions. Providing an alternative explana- 
tion would not be possible without the explicit 
representation of the intentional structure of 
the generated text. Note that it is important 
to record the rhetorical structure as well, so 
that the text planner can choose an alterna- 
tive rhetorical strategy for achieving the goal. 
In the example under consideration, the re- 
covery heuristics indicate that the rhetorical 
strategy of giving examples should be chosen. 
RELATED WORK 
Schemata (McKeown, 1985) encode standard 
patterns of discourse structure, but do not in- 
dude knowledge of how the various parts of 
a schema relate to one another or what their 
intended effect on the hearer is. A schema 
can be viewed as a compiled version of one 
of our text plans in which all of the non- 
terminal nodes have been pruned out and only 
the leaves (the speech acts) remain. While 
schemata can produce the same initial behav- 
ior as one of our text plans, all of the ratio- 
nale for that behavior has been compiled out. 
Thus schemata cannot be used to participate 
in dialogues. If the user indicates that he has 
not understood the explanation, the system 
cannot know which part of the schema failed 
to achieve its effect on the hearer or which 
rhetorical strategy failed to achieve this ef- 
fect. Planning a text using our approach is 
essentially planning a: schema from more fine- 
grained plan operators. From a library of such 
plan operators, many varied schemata can re- 
sult, improving the flexibility of the system. 
In an approach taken by Cohen and Ap- 
pelt (1979) and Appelt (1985), text is planned 
by reasoning about the beliefs of the hearer 
and speaker and the effects of surface speech 
aWe are also currently implementing another in- 
terface which allows users to use a mouse to point at 
the noun phrases or clauses in the text that were not 
understood {Moore, 1989b). 
acts on these beliefs (i.e., the intentional ef- 
fect). This approach does not include rhetori- 
cal knowledge about how clausal units may be 
combined into larger bodies of coherent text 
to achieve a speaker's goals. It assumes that 
appropriate axioms could be added to gen- 
erate large (more than one- or two-sentence) 
bodies of text and that the text produced will 
be coherent as a by-product of the planning 
process. However, this has not been demon- 
strated. 
Itecently, Hovy (1988b) built a text struc- 
turer which produces a coherent text when 
given a set of inputs to express. Hovy uses 
an opportunistic planning approach that or- 
ders the inputs according to the constraints 
on the rhetorical relations defined in Rhetori- 
cal Structure Theory. His approach provides a 
description of what can be said when, but does 
not include information about why this infor- 
mation can or should be included at a partic- 
ular point. Hovy's approach confiates inten- 
tional and rhetorical structure and, therefore, 
a system using his approach could not later 
reason about which rhetorical strategies were 
used to achieve intentional goals. 
STATUS AND FUTURE WORK 
The text planner presented is imple.mented in Common Lisp and can produce the text 
plans necessary, to participate in the sample ~lialogue described m this paper and several 
others (see (Moore, 1989a, Paris, 1988a)). We 
currently have over 60 plan operators and the system can answer tlie following types of 
(follow-up) questions: 
- Why? - Why conclusion? 
- Why are you trying to achieve goal? 
- Why are you using method to achieve goal? Why are you doing act? 
How do you achieve goal? 
- How did you achieve goal (in this case)? 
- What is a concept? 
- What is the difference between concept1 and concept2? 
- Huh? 
The text planning system described in this 
paper is being incorporated into two expert 
systems currently under development. These 
systems will be installed and used in the field. 
This will give us an opportunity to evaluate 
the techniques proposed here. 
We are currently studying how the atten- 
tional structure inherent in our text plans can 
be used to guide the realization process, for 
example in the planning of referring expres- 
sions and the use of cue phrases and pronouns. 
We are also investigating criteria for the ex- 
pansion and ordering of optional satellites in 
our plan operators. Currently we use informa- 
tion from the user model to dictate whether 
or not optional satellites are expanded, and 
their ordering is specified in each plan opera- 
tor. We wish to extend our criteria for satel- 
lite expansion to include other factors such as 
pragmatic and stylistic goals (Hovy, 1988a) 
(e.g., brevity) and the conversation that has 
occurred so far. We are also investigating the 
use of attentional information to control the 
ordering of these satellites (McKeown, 1985). 
We also believe that the detailed text plan 
constructed by our planner will allow a system 
to modify its strategies based on experience 
(feedback from the user). In (Paris, 1988a), 
we outline our preliminary ideas on this issue. 
We have also begun to study how our planner 
can be used to handle incremental generation 
of texts. In (Moore, 1988), we argue that the 
detailed representation provided by our text 
plans is necessary for execution monitoring 
and to indicate points in the planning process 
where feedback from the user may be helpful 
in incremental text planning. 
CONCLUSIONS 
In this paper, we have presented a text plan- 
ner that builds a detailed text plan, contain- 
ing the intentional, attentional, and rhetor- 
ical structures of the responses it produces. 
We argued that, in order to participate in a 
dialogue with its users, a generation system 
must be capable of reasoning about its past 
utterances. The text plans built by our text 
planner provide a generator with the infor- 
mation needed to reason about its responses. 
We illustrated these points with a sample di- 
alogue. 
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