A TRIPARTITE PLAN-BASED MODEL OF DIALOGUE 
Lynn Lambert 
Sandra Carberry 
Department of Computer and Information Sciences 
University of Delaware 
Newark, Delaware 19716, USA 
Abstract 1 
This paper presents a tripartite model of dialogue in 
which three different kinds of actions are modeled: 
domain actions, problem-solving actions, and dis- 
course or communicative actions. We contend that 
our process model provides a more finely differenti- 
ated representation of user intentions than previous 
models; enables the incremental recognition of com- 
municative actions that cannot be recognized from 
a single utterance alone; and accounts for implicit 
acceptance of a communicated proposition. 
1 Introduction 
This paper presents a tripartite model of di- 
alogue in which intentions are modeled on three 
levels: the domain level (with domain goals such as 
traveling by train), the problem-solving level (with 
plan-construction goals such as instantiating a pa- 
rameter in a plan), and the discourse level (with 
communicative goals such as ezpressing surprise). 
Our process model has three major advantages over 
previous approaches: 1) it provides a better repre- 
sentation of user intentions than previous models 
and allows the nuances of different kinds of goals 
and processing to be captured at each level; 27 it 
enables the incremental recognition of commumca- 
tire goals that cannot be recognized from a single 
utterance alone; and 3) it differentiates between il- 
locutionary effects and desired perlocutionary ef- 
fects, and thus can account for the failure of an 
inform act to change a heater's beliefs\[Per90\] ~. 
2 Limitations of Current 
Models of Discourse 
A number of researchers have contended 
that a coherent discourse consists of segments 
that are related to one another through some 
type of structuring relation\[Gri75, MT83\] or have 
used rhetorical relations to generate coherent 
text\[Hov88, MP90\]. In addition, some researchers 
1 This material is based upon work supported by the Na- 
tional Science Foundation under Grant No. IRI-8909332. 
The Government has certain rights in this material. 
2We would ilke to thank Kathy McCoy for her comments 
on various drafts of this paper. 
have modeled discourse based on the semantic rela- 
tionship of individual clauses\[Po186a\] or groups of 
clauses\[Rei78\]. But all of the above fail to capture 
the goal-oriented nature of discourse. Grosz and 
Sidner\[GS86\] argue that recognizing the structural 
relationships among the intentions underlying a dis- 
course is necessary to identify discourse structure, 
but they do not provide the details of a compu- 
tational mechanism for recognizing these relation- 
ships. 
To account for the goal-oriented nature of 
discourse, many researchers have adopted the 
planning/plan-recognition paradigm\[APS0, PA80\] 
in which utterances are viewed as part of a plan 
for accomplishing a goal and understanding con- 
sists of recognizing this plan. The most well- 
developed plan-based model of discourse is that of 
Litman and AIIen\[LA87\]. However, their discourse 
plans conflate problem-solving actions and commu- 
nicative actions. For example, their Correct-Plan 
has the flavor of a problem-solving plan that one 
would pursue in attempting to construct another 
plan, whereas their Identify-Parameter takes on 
some of the characteristics of a communicative plan 
that one would pursue when conveying information. 
More significantly, their model cannot capture the 
relationship among several utterances that are all 
part of the same higher-level discourse plan if that 
plan cannot be recognized and added to their plan 
stack based on analysis of the first utterance alone. 
Thus, if more than one utterance is necessary to 
recognize a discourse goal (as is often the case, for 
example, with warnings), Litman and Allen's model 
will not be able to identify the discourse goal pur- 
sued by the two utterances together or what role 
the first utterance plays with respect to the sec- 
ond. Consider, for example, the following pair of 
utterances: 
(1) The city of zz~ is considering filing for 
bankruptcy. 
(2) One of your mutual funds owns zzz bonds. 
Although neither of the two utterances alone con- 
stitutes a warning, a natural language system must 
be able to recognize the warning from the set of two 
utterances together. 
Our tripartite model of dialogue overcomes 
these limitations. It differentiates among domain, 
problem-solving, and communicative actions yet 
models the relationships among them, and enables 
47 
the recognition of communicative actions that take 
more than one utterance to achieve but which can- 
not be recognized from the first utterance alone. 
In the remainder of this paper, we will 
present our tripartite model, motivating why our 
model recognizes three different kinds of goals, de- 
scribing our dialogue model and how it is built in- 
crementally as a discourse proceeds, and illustrat- 
ing this plan inference process with a sample dia- 
logue. Finally, we will outline our current research 
on modeling negotiation dialogues and recognizing 
discourse acts such as expressing surprise. 
3 A Tripartite Model 
3.1 Kinds of Goals and Plans 
Our plan recognition framework recognizes 
three different kinds of goals: domain, problem- 
solving, and discourse. In an information-seeking 
or expert-consultation dialogue, one participant is 
seeking information and advice about how to con- 
struct a plan for achieving some domain goal. A 
problem-solving goal is a metagoal that is pursued 
in order to construct a domain plan\[Wil81, LA87, 
Ram89\]. For example, if an agent has a goal of 
earning an undergraduate degree, the agent might 
have the problem-solving goal of selecting the in- 
stantiation of the degree parameter as BA or BS 
and then the problem-solving goal of building a sub- 
plan for satisfying the requirements for that degree. 
A number of researchers have demonstrated the im- 
portance of modeling domain and problem-solving 
goals\[PA80, WilS1, LA87, vBC86, Car87, Ram89\]. 
Intuitively, a discourse goal is the com- 
municative goal that a speaker has in making an 
utterance\[.Car89\], such as obtaining information 
or expressing surprise. Recognition of discourse 
goals provides expectations for subsequent utter- 
ances and suggests how these utterances should be 
interpreted. For example, the first two utterances 
in the following exchange establish the expectation 
that S1 will either accept S2's response, or that S1 
will pursue utterances directed toward understand- 
ing and accepting it\[Car89\]. Consequently, Sl's sec- 
ond utterance should be recognized as expressing 
surprise at S2's statement. 
SI: When does CS400 meet? 
$2:GS400 meets on Monday from 7.9p.m. 
SI: GS400 meets at night? 
A robust natural language system must recognize 
discourse goals and the beliefs underlying them in 
order to respond appropriately. 
The plan library for our process model con- 
tains the system's knowledge of goals, actions, and 
plans. Although domain plans are not mutually 
known by the participants\[Po186b\], how to commu- 
nicate and how to solve problems are common skills 
that people use in a wide variety of contexts, so 
the system can assume that knowledge about dis- 
course and problem-solving plans is shared knowl- 
edge. Our representation of a plan includes a 
header giving the name of the plan and the action 
it accomplishes, preconditions, applicability condi- 
tions, constraints, a body, effects, and goals. Appli- 
cability conditions represent conditions that must 
be satisfied for the plan to be reasonable to pur- 
sue in the given situation whereas constraints limit 
the allowable instantiation of variables in each of 
the components of a plan\[LAB7, Car87\]. Especially 
in the case of discourse plans, the goals and effects 
are likely to be different. This allows us to dif- 
ferentiate between illocutionary and perlocutionary 
effects and capture the notion that one can, for ex- 
ample, perform an inform act without the hearer 
adopting the communicated proposition. 3 Figure 1 
presents three discourse plans and one problem- 
solving and domain plan. 
3.2 Structure of the Model 
Agents use utterances to perform commu- 
nicative acts, such as informing or asking a ques- 
tion. These discourse actions can in turn be part 
of performing other discourse actions; for example, 
providing background data can be part of asking 
a question. Discourse actions can take more than 
one utterance to complete; asking for information 
requires that a speaker request the information and 
believe that the request is acceptable (i.e., that the 
speaker say enough to ensure that the speaker be- 
lieves that the request is understandable, justified, 
and the necessary background information is known 
by the respondent). Thus, actions at the discourse 
level form a tree structure in which each node rep- 
resents a communicative action that a participant 
is performing and the children of a node represent 
communicative actions pursued in order to perform 
the parent action. 
Information needed for problem-solving ac- 
tions is obtained through discourse actions, so dis- 
course actions can be executed in order to perform 
problem-solving actions as well as being part of 
other discourse actions. Similarly, domain plans 
are constructed through problem-solving actions, so 
problem-solving actions can be executed in order to 
eventually perform domain actions as well as being 
part of plans for other problem-solving actions. 
Therefore, our Dialogue Model (DM) con- 
tains three levels of tree structures, 4 one for each 
kind of action (discourse, problem-solving, and do- 
main) with links among the actions on different lev- 
els. At the lowest level the discourse actions are 
represented; these actions may contribute to the 
problem-solving actions at the middle level which, 
ZConsider, for example, someone saying "I informed yon 
of X 6at you wouldn't 6elieve me." 
4The DM is really a mental model of intentions\[Pol80b\]. 
The structures shown in our figures implicitly capture a num- 
ber of intentions that are attributed to the participants, such 
as the intention that the hearer recognize that the speaker 
believes the applicability conditions for the just initiated dis- 
course actions are satisfied and the intention that the par- 
ticipants follow through with the subactions that are part of 
plans for actions in theDM. 
48 
Domain Plan-D1: {_agent earns a minor in _subj} 
Action: Get-Minor(.agent, ..sub j) 
Prec: have-plan(_agent, Plan-D1, Get-minor(.agent, .sub j)) 
Body: 1. Complete-Form(.agent, change-of-major-form, add-minor) 
2. Take-Required-Courses(.agent, .sub j) 
Effects: have-minor(_agent, -sub j) 
Goal: have-minor(_agent, _sub j) 
Action: 
AppCond: 
Constr: 
Problem-solvin~ Plan-P1:{_agent1 and _agent~ build a plan \]or -agent1 to do _action} 
Build-Plan(_agentl, .agent2, .,action) 
want(.agentl, .action) 
plan-for(.plan, .action) 
action-in-plan-for(Aaction, .action) 
Prec: selected(_agentl, .action, .plan) 
know(.agent2, want(.agentl, .action)) 
knowref(..agentl, .prop, prec-of(.prop, -plan)) 
knowref(.agent2, .prop, prec-of(-prop, .plan)) 
knowref(.agentl, .\]action, need-do(.agentl, .laction, .action)) 
knowref(_agent2, .laction, need-do(-agentl, Aaction, .action)) 
1. for all actions .laction in .plan, Instantiate-Vars(-agentl, .agent2, _laction) 
2. for all actions .laction in -plan, Build-Plan(.agentl, .agent2, .laction) 
have-plan(_agentl, .plan, .action) 
have-plan(-agentl, .plan, .action) 
Body: 
Effects: 
Goal: 
Discourse Plan-C1: {_agentl asks -agent~ /or the values of.term \]or which -prop is true} 
Action: Ask-Ref(.agentl, .agent2, .term, .prop) 
AppCond: want(-agentl, knowref(-agentl, _term, believe(.agent2, -prop))) 
--knowref(.agentl, .term, believe(.agent2, .prop)) 
Constr: term-in(_term, .prop) 
Body: Request(_agentl, .agent2, Informref(_agent2, .agentl, _term, ..prop)) 
Make-Question-Acceptable(_agentl, _agent2, _prop) 
Effects: believe(-agent2, want(.agentl, Informref(.agent2, .agent1, _term, .prop))) 
(goal: want(.agent2, Answer-Ref(.agent2, .agent1, -term, _prop)) 
Discourse Plan-C2:{-agent1 in\]orms _agent2 o\] _prop} 
Action: Inform(.agentl, .agent2, .prop) 
AppCond: believe(.agentl, know(-agentl, .prop)) 
-,believe(.agentl, believe(.agent2, .prop)) 
Body: Tell(.,xgentl, .agent2, .prop) 
Make-Prop-Believable(.agentl, .agent2, .prop) 
Effects: believe(.agent2, want(.agentl, believe(.agent2, -prop))) 
Goal: know(.,xgent2, .prop) 
Discourse Plan-C3:{_agent1 tells _prop to .agent~} 
Action: Tell(.agentl, .agent2, .prop) 
AppCond: believe(.agentl, .prop) 
-~believe(_agentl, believe(.agent2, believe(.agentl, .prop))) 
Body: Surface-Inform(.agentl, .agent2, .prop) 
Make-Statement-Understood(_agentl, .agent2, -prop) 
Effects: told-about(_agent2, .prop) 
Goal: believe(.agent2, believe(-agentl, .prop)) 
Figure 1: Sample Plans from the Plan Library 
49 
in turn, may contribute to the domain actions at 
the highest level (see Figure 3). The planning agent 
is the agent of all actions at the domain level, since 
the plan being constructed is for his subsequent ex- 
ecution. Since we are assuming a cooperative di- 
alogue in which the two participants are working 
together to construct a domain plan, both partic- 
ipants are joint agents of actions at the problem- 
solving level. Both participants make utterances 
and thus either participant may be the agent of an 
action at the discourse level. 
For example, a DM derived from two ut- 
terances is shown in Figure 3; its construction is 
described in Section 3.3. The DM in Figure 3 in- 
dicates that the inform and the request were both 
part of a plan for asking for information; the inform 
provided background data enabling the information 
request to be accepted by the hearer. Furthermore, 
the actions at the discourse level were pursued in or- 
der to perform a Build-Plan action at the problem- 
solving level, and this problem-solving action is be- 
ing performed in order to eventually perform the 
domain action of getting a math minor. The cur- 
rent focus of attention on each level is marked with 
an asterisk. 
3.3 Building the Dialogue Model 
Our process model uses plan inference 
rules\[APS0, Car87\], constraint satisfaction\[LAB7\], 
focusing heuristics\[Car87\], and features of the new 
utterance to identify the relationship between the 
utterance and the existing dialogue model. The 
plan inference rules take as input a hypothesized 
action Ai and suggest other actions (either at the 
same level in the DM or at the immediately higher 
level) that might be the agent's motivation for Ai. 
The focusing heuristics order according to 
coherence the ways in which the DM might be ex- 
panded on each of the three levels to incorporate 
the actions motivating a new utterance. Our focus- 
ing heuristics at the discourse level are: 
1. Expand the plan for an ancestor of the cur- 
rently focused action in the existing DM so 
that it includes the new utterance, preferring 
to expand ancestors closest to the currently fo- 
cused action. This accounts for new utterances 
that continue discourse acts already in the DM. 
2. Enter a new discourse action whose plan can 
be expanded to include both the existing dis- 
course level of the DM and the new utterance. 
This accounts for situations in which actions 
at the discourse level of the previous DM are 
part of a plan for another discourse act that 
had not yet been conveyed. 
3. Begin a new tree structure at the discourse 
level. This accounts for initiation of new dis- 
course plans unrelated to those already in the 
DM. 
The focusing heuristics, however, are not 
identical for all three levels. Although it is not pos- 
sible to expand the plan for the focused action on 
the discourse level since it will always be a surface 
speech act, continuing the plan for the currently 
focused action or expanding it to include a new 
action are the most coherent expectations on the 
problem-solving and domain levels. This is because 
the agents are most expected to continue with the 
problem-solving and domain plans on which their 
attention is currently centered. In addition, since 
actions at the discourse and problem-solving lev- 
els are currently being executed, they cannot be 
returned to (although a similar action can be initi- 
ated anew and entered into the model). However, 
since actions at the domain level are part of a plan 
that is being constructed for future execution, a 
domain subplan already completely developed may 
be returned to for revision. Although such a shift 
in attention back to a previously considered sub- 
plan is not one of the strongest expectations, it is 
still possible at the domain level. Furthermore, new 
and unrelated discourse plans will often be pursued 
during the course of a conversation whereas it is un- 
likely that several different domain plans (each rep- 
resenting a topic shift) will be investigated. Thus, 
on the domain level, a return to a previously con- 
sidered domain subplan is preferred over a shift to 
a new domain plan that is unrelated to any already 
in the DM. 
In addition to different focusing heuristics 
and different agents at each level, our tripartite 
model enables us to capture different rules regard- 
ing plan retention. A continually growing dialogue 
structure does not seem to reflect the information 
retained by humans. We contend that the domain 
plan that is incrementally fleshed out and built at 
the highest level should be maintained through- 
out the dialogue, since it provides knowledge about 
the agent's intended domain actions that will be 
useful in providing cooperative advice. However, 
problem-solving and discourse actions need not be 
retained indefinitely. If a problem-solving or dis- 
course action has not yet completed execution, then 
its immediate children should be retained in the 
DM, since they indicate what has been done as part 
of performing that as yet uncompleted action; its 
other descendants can be discarded since the apar- 
ent actions that motivated them are finished. (For 
illustration purposes, all actions have been retained 
in Figure 3.) 
We have expanded on Litman and Allen's 
notion of constraint satisfaction\[LA87\] and Allen 
and Perrault's use of beliefs\[AP80\]. Our applica- 
bility conditions contain beliefs by the agent of the 
plan, and our recognition algorithm requires that 
the system be able to plausibly ascribe these beliefs 
in recognizing the plan. The algorithm is given the 
semantic representation of an utterance. Then plan 
inference rules are used to infer actions that might 
motivate the utterance; the belief ascription process 
during constraint satisfaction determines whether 
it is reasonable to ascribe the requisite beliefs to 
the agent of the action and, if not, the inference 
is rejected. The focusing heuristics allow expecta- 
50 
tions derived from the existing dialogue context to 
guide the recognition process by preferring those 
inferences that can eventually lead to the most ex- 
pected expansions of the existing dialogue model. 
In \[Car89\] we claimed that a cooperative 
participant must explicitly or implicitly accept a 
response or pursue discourse goals directed toward 
being able to accept the response. Thus our model 
treats failure to initiate a negotiation dialogue as 
implicit acceptance of the proposition conveyed by 
the response. Consider, for example, the following 
dialogue: 
SI: Who is teaching CS360 next semester? 
$2: Dr. Baker. 
SI: What time does it meet? 
Since Sl's second utterance cannot be interpreted 
as initiating a negotiation dialogue, S1 has implic- 
itly accepted the proposition that Dr. Baker is 
teaching CS360 next semester as true. This no- 
tion of implicit acceptance is similar to a restricted 
form of Perrault's default reasoning about the ef- 
fects of an inform act\[Per90\] and is explained fur- 
ther in \[Lam91\]. 
3.4 An Example 
As an example of how our process model 
assimilates utterances and can incrementally rec- 
ognize a discourse action that cannot be recognized 
from a single utterance, consider the following: 
SI: (a) I want a math minor. 
(b) What should I do? 
A few of the plans needed to handle this example 
are shown in Figure 1; these plans assume a co- 
operative dialogue. From the surface inform, plan 
inference rules suggest that S1 is executing a Tell 
action and that this Tell is part of an Inform ac- 
tion (the applicability conditions for both actions 
can be plausibly ascribed to S1) and these are en- 
tered into the discourse level of the DM. No fur- 
ther inferences on this level are possible since the 
Inform can be part of several discourse plans and 
there is no existing dialogue context that suggests 
which of these S1 might be pursuing. The system 
infers that S1 wants the goal of the Inform action, 
namely know(S2, want(S1, Get-Minor(S1, Math))). 
Since this proposition is a precondition for building 
a plan for getting a math minor, the system infers 
that S1 wants Build-Plan(S1, $2, Get-Minor(S1, 
math)) and this Build.Plan action is entered into 
the problem-solving level of the DM. From this, the 
system infers that S1 wants the goal of that action; 
since this result is the precondition for getting a 
math minor, the system infers that S1 wants to get 
a math minor and this domain action is entered into 
the domain level of the DM. The resulting discourse 
model, with links between the actions at different 
levels and the current focus of attention on each 
level marked with an asterisk, is shown in Figure 2. 
The semantic representation of (b) is 
jR 
| 
| * \[Build-Plan~Sl, S2, Get-Minor,S1, 
DomLin Level f''''" "''''''J 
! ! " \[Oct-Minor{S,, M&th~ \] I g T 
en~ble-&rc 
Problem-solvlng Level 
~.,h?? J , 
Discourse Level m .5 jdmm~en~o em em am amamm 
| ! 
g 8ub&ctioa-sre ~ J | ! ! 
, \[ T,n{s~, s~, ...~{s~, Q,,-Mi.o,(S~, M.,b), I t 
J sub6ct\[on-&r¢ ~ J ! ! 
Figure 2: Dialogue Model from the first utterance 
Surface-Request(S1, $2, Informref(S2, $1, 
_action1, need-do(S1, _action1, .action2))) 
From this utterance we can infer that $1 is per- 
forming a Request and thus may be performing an 
Ask.Re? action (since Request is part of the body 
of the plan for Ask-Re~) and that S1 may thus be 
performing an Obtain-Info-Ref action (since Ask- 
Re? is part of the body of the plan for Obtain-In?o- 
Re\]) and that S1 wants the goal of the Obtain-In?o- 
Re? action (namely, that $1 know the subactions 
that he needs to do in order to perform _a~tion2), 
which is in turn a precondition for building a plan. 
This produces the inference that $1 wants Build- 
Plan(S1, $2, .action2) which is an action at the 
problem-solving level. 
The focusing heuristics suggest that the 
most coherent expectation at the discourse level is 
that Sl's discourse level actions are part of a plan 
for performing the Tell action that is the parent 
of the action that was previously marked as the 
current focus of attention in the discourse model. 
However, no line of inference from the second ut- 
terance represents an expansion of this plan. (This 
means that the proposition was understood without 
any clarification. 5) Similarly, no expansion of the 
plan for the Inform action (the other ancestor of the 
focus of attention in the existing DM) succeeds in 
linking the new utterance to the DM. (This means 
that the communicated proposition was accepted 
without any squaring away of beliefs\[Jos82\].) 
Since the first focusing heuristic was unsuc- 
cessful in finding a relationship between the new 
utterance and the existing dialogue model, the sec- 
5We are assuming that the hearer has an opportunity to 
intervene after an utterance. This is a simplification and 
must eventually be removed to capture a heater's saving his 
requests for clarification and negotiation of beliefs until the 
end of the speaker's complete turn. 
51 
en~ble-&rc 
Dom.in Level f, .q 
| | ~* \[ Oct-Minor{St, }~\[sth) J | i 
en~ble-~r¢ 
Problem-solvlng Level 
| J 
8 ~ IBuild-Plzn(51 53 Get-Mluor(Sl M~th)) : 4 : '.-} 
ensble-Lrc | m m m 
I rmm~mmmmm 
i O bt.~-l~fo-Ref(St. $2. -&ctlonl. need-dotS1..Lctlonl. Oct-Minor(St. M~.th))) 
sub,ction-&rc ¢ 
\[ A.k-I~ef~$1, $2, .Actionl .... d-do~Sl, Get.~Iinor~Sl, l~.th))) 
sub.ction-src ¢ .~ctlon I, 
\[ M.ke-qu.s*lon-*¢~-p~.bie~S~, s~ .... d-do~s~, Oe~-Mi~o.~S~, ~.tb))) I snbsction-&r¢ ¢ .~ctionl, 
' t Glve-Bzckg .... d($1, 52 .... t(SI, Oet-lviinor(Sl, Msth)), I J \[ Iteed-do(Sl, .~ctlonl, Oct-Minor(S1, M~th))) J 
J J sub&ction-.rc ¢ 
| , \[ lnform~Sl, $2 ..... ~Sl, CJet-MinorISl, l~4&th))) J 
i sub,Lction-t,r¢ ¢ 
| need-do{S1! 
| sub.ctlon-&rc 
I \] Surf .... ,ztform{Sl, $2 .... t($1, Oet-Minor(Sl, Ms|h))) \] . J 
I I need-do{St, I 
Discourse Level 
sub.ctlon-,rc 
l 
l 
l 
l 
l 
I 
l 
t 
l 
l 
l 
J 
l 
l 
i 
I' l luformrefq S~, Sl, .~ctionl, .sctlont t }ei-Minor(Sl. MAth'})) 
subtction-src I ! 
Surf&ce-J~.equest(Sl, S2s lnfotmref(S2, SI, .sctionl, | J 
.~¢tionl? Oet.lCfiuor{Sl! MAth}}} \] J 
......J 
Figure 3: Dialogue Model derived from two utterances 
ond focusing heuristic is tried. It suggests that the 
new utterance and the actions at the discourse level 
in the existing DM might both be part of an ex- 
panded plan for some other discourse action. The 
inferences described above lead from (b) to the dis- 
course action Ask-Ref whose plan can be expanded 
as shown in Figure 3 to include, as background for 
the Ask-Ref, the Inform and the Tell actions that 
were entered into the DM from (a). s The focusing 
heuristics suggest that the most coherent continu- 
ation at the problem-solving level is that the new 
utterance is continuing the Build-Plan that was pre- 
viously marked as the current focus of attention at 
that level. This is possible by instantiating .action2 
with Get-minor(S1, math). Thus the DM is ex- 
panded as shown in Figure 3 with the new focus 
of attention on each level marked with an asterisk. 
Note that Sl's overall goal of obtaining information 
was not conveyed by (a) alone; consequently, only 
after both utterances were coherently related could 
it be determined that (a) was paxt of an overall 
discourse plan to obtain information and that (a) 
was intended to provide background data for the 
request being made in (b). 7 
6Note that the actions in the body of Ask.Re\] ~re not 
ordered; an agent can provide d~'ification and background 
information before or after asking a question. 
7An inform action could also be used for other pur- 
poses, including justifying a question and merely conveying 
information. 
Further queries would lead to more elaborate 
tree structures on the problem-solving and domain 
levels. For example, suppose that S1 is told that 
Math 210 is a required course for a math minor. 
Then a subsequent query such as Who is teach- 
ing Math 210 next semester e. would be performing 
a discourse act of obtaining information in order 
to perform a problem-solving action of instantiat- 
ing a parameter in a Learn-Material domain action. 
Since learning the materiM from one of the teach- 
ers of a course is part of a domain plan for taking a 
course and since instantiating the parameters in ac- 
tions in the body of domain plans is part of building 
the domain plan, further inferences would indicate 
that this Instanfiafe- Wars problem-solving action is 
being executed in order to perform the problem- 
solving action of building a plan for the domain 
action of taking Math 210 in order to build a plan 
to get a math minor. Consequently, the domain 
and problem-solving levels would be expanded so 
that each contained several plans, with appropriate 
links between the levels. 
4 Current and Future Work 
We are currently examining the applications 
that this model has in modeling negotiation dia- 
logues and discourse acts such as convince, warn, 
and express surprise. To extend our notion of im- 
plicit acceptance of a proposition to negotiation di- 
52 
alogues, we are exploring treating a discourse plan 
as having successfully achieved its goal if it is plau- 
sible that all of its subacts have achieved their goals 
and all of its applicability conditions (except those 
negated by the goal) are still true after the subacts 
have been executed. 
Especially in negotiation dialogues, a system 
must account for the fact that a user may change 
his mind during a conversation. But often people 
only slightly modify their beliefs. For example, the 
system might inform the user of some proposition 
about which the user previously held no beliefs. In 
that case, if the user has no reason to disbelieve the 
proposition, the user may adopt that proposition as 
one of his own beliefs. However, if the user disbe- 
lieved the proposition before the system performed 
the inform, then the user might change from disbe- 
lief to neither belief nor disbelief; a robust model of 
understanding must be able to handle a response 
that expresses doubt or even disbelief at a previous 
utterance, especially in modeling arguments and 
negotiation dialogues. Thus, a system should be 
able to (1) represent levels of belief, (2) recognize 
how a speaker's utterance conveys these different 
levels of belief, (3) use these levels of belief in recog- 
nizing discourse plans, and (4) use previous context 
and a user's responses to model changing beliefs. 
We are investigating the use of a multi-level 
belief model to represent the strength of an agent's 
beliefs and are studying how the form of an utter- 
ance and certain clue words contribute to conveying 
these beliefs. Consider, for example, the following 
two utterances: 
(1) Is Dr. Smith teaching CSMO? 
(2) Isn't Dr. Smith teaching CSMO? 
A simple yes-no question as in utterance (1) sug- 
gests only that the speaker doesn't know whether 
Dr. Smith teaches CS310 whereas the form of the 
question in utterance (2) suggests that the speaker 
has a relatively strongbelief that Dr. Smith teaches 
CS310 but is uncertain of this. These beliefs con- 
veyed by the surface speech act must be taken into 
account during the plan recognition process. Thus 
our plan recognition algorithm will first use the ef- 
fects of the surface speech act to suggest augmen- 
tations to the belief model. These augmentations 
will then be taken into account in deciding whether 
requisite beliefs for potential discourse acts can be 
plausibly ascribed to the speaker and will enable us 
to identify such discourse actions as expressing sur- 
prise. \[Lam91\] further discusses the use of a multi- 
level belief model and its contribution in modeling 
dialogue. 
cution level (corresponding to queries after commit- 
ment has been made to achieve a goal in a particular 
way). In our tripartite model, discourse, problem- 
solving, and domain plans form a hierarchy with 
links between adjacent levels. Whereas Ramshaw's 
exploration level captures the consideration of al- 
ternative plans, our intermediate level captures the 
notion of problem-solving and plan-construction, 
whether or not there has been a commitment to 
a particular way of achieving a domain goal. Thus 
a query such as To whom do I make out the check? 
would be recognized as a query against the domain 
execution level in Ramshaw's model (since it is a 
query made after commitment to a plan such as 
opening a passbook savings account\[Ram91\]), but 
our model would treat it as a discourse plan that 
is executed to further the problem-solving plan of 
instantiating a parameter in an action in a domain 
plan -- i.e., our model would view the agent as ask- 
ing a question in order to further the construction 
of his partially constructed domain plan. 
Our tripartite model offers several advan- 
tages. Ramshaw's model assumes that the top-level 
domain plan is given at the outset of the dialogue 
and then his model expands that plan to accom- 
modate user queries. Our model, on the other 
hand, builds the DM incrementally at each level 
as the dialogue progresses; it therefore can han- 
dle bottom-up dialogues\[Car87\] in which the user's 
overall top-level goal is not explicitly known at the 
outset and can recognize discourse actions that can- 
not be identified from a single utterance. In addi- 
tion, our domain, problem-solving, and discourse 
plans are all recognized incrementally using basi- 
cally the same plan recognition algorithm on each 
level\[Wil81\]. Consequently, we foresee being able 
to extend our model to include additional pairs of 
problem-solving and discourse levels whose domain 
level contains an existing problem-solving or dis- 
course plan; this will enable us to handle utter- 
ances such as What should we work on next? (query 
trying to further construction of a problem-solving 
plan) and Do you have information about ... ? 
(query trying to further construction of a discourse 
plan to obtain information). 
Ramshaw's plan exploration strategies, his 
differentiation between exploration and commit- 
ment, and his heuristics for recognizing adoption of 
a plan are very important. While our work has not 
yet addressed these issues, we believe that they are 
consistent with our model and are best addressed at 
our problem-solving level by adding new problem- 
solvin~ metaplans. Such an incorporation will have 
severat advantages, including the ability to handle 
utterances such as 
5 Related Work 
Ramshaw\[Ram91\] has developed a model of 
discourse that contains a domain execution level, 
an exploration level, and a discourse level. In his 
model, discourse plans can refer either to the explo- 
ration level (corresponding to queries about possi- 
ble ways of achieving a goal) or to the domain exe- 
If I decide to get a BA degree, then I'll take 
French to meet the foreign language requirement. 
In the above case, the speaker is still exploring a 
plan for getting a BA degree, but has committed 
to taking French to satisfy the foreign language re- 
quirement should the plan for the BA degree be 
adopted. It does not appear that Ramshaw's model 
53 
can handle such contingent commitment. This en- 
richment of our problem-solving level may necessi- 
tate changes to our focusing heuristics. 
6 Conclusions 
We have presented a tripartite model of dia- 
logue that distinguishes between domain, problem- 
solving, and discourse or communicative actions. 
By modeling each of these three kinds of actions 
as separate tree structures, with links between the 
actions on adjacent levels, our process model en- 
ables the incremental recognition of discourse ac- 
tions that cannot be identified from a single ut- 
terance alone. However, it is still able to cap- 
ture the relationship between discourse, problem- 
solving, and domain actions. In addition, it pro- 
vides a more finely differentiated representation of 
user intentions than previous models, allows the nu- 
ances of different kinds of processing (such as dif- 
ferent focusing expectations and information reten- 
tion) to be captured at each level, and accounts for 
implicit acceptance of a communicated proposition. 
Our current work involves using this model to han- 
dle negotiation dialogues in which a hearer does not 
automatically accept as valid the proposition com- 
municated by an inform action. 
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