DPOCL: A Principled Approach to Discourse Planning 
R. Michael Young 
• Intelligent Systems Program 
University of Pittsburgh 
Pittsburgh, PA, 15260 
myoung+epJ.tt, edu 
,\]ohanna D. Moore 
Department of Computer Science and 
Learning Research and Development Center 
University of Pittsburgh 
Pittsburgh, PA 15260 
jmoore@cs, pit~. edu 
Abstract 
Research in discourse processing has ident~ed two rep- 
resentational requirements for discourse planning sys- 
tems. First, discourse plans must adequately represent 
the intentional structure of the utterances they produce 
in order to enable a computations.\] discourse agent to 
respond effectively to communicative failures \[15\]. Sec- 
ond, discourse plans must represent the information~ 
structure of utterances. In addition to these represen- 
tational requirements, we argue that discourse planners 
ahonid be formally characterizab\]e in terms of soundness 
and completeness. 
1 Introduction 
Research in discourse processing has identified two repre- 
sentational requirements for discourse planning systems. 
First, discourse plans must adequately represent the ino 
tentionai structure of the utterances they produce in 
order to enable a computational discourse agent to re- 
spond effectively to communicative failures \[15\]. Second, 
discourse plans must represent the informational struc- 
ture of utterances. Discourse interpretation requires that 
an agent be able to recognize the relationships between 
the information conveyed in consecutive elements of dis- 
course (e.g., \[7, 16\]). Choosing syntactic structures and 
connective markers that convey these relationships re- 
quires that a discourse generator represent informational 
\[19, 21, 22\] as well as intentional \[4\] structure. Because 
there is not a fixed, one-to-one mapping between inten- 
tional and informational structures, discourse plans must 
include an explicit representation of both types of struc- 
ture \[15, 16\]. 
In addition to these representational requirements, we 
argue that discourse planners should meet certain com- 
putational requirements. Most current discourse plan- 
ners are based on the original NOAH \[20\] model of hi- 
erarchical planning \[1, 2, 9, 13, 15\]. These systems rely 
on customized planning algorithms with procedural se- 
mantics for the purposes of solving specific text-planning 
problems. The informal construction of these systems 
and their application to particular problems have re- 
suited in successful text generation for specific domains 
and text types. However, careful analysis of these pro- 
grams shows that there is nothing in their semantics to 
prevent them from generating incorrect plans, generat- 
ing plans with redundant steps, or failing to find plans 
in situations where they exist. To the extent that these 
planners have been able to avoid these problems, they 
have done so by severely limiting the expressive power 
of action descriptions and/or requiring the designer to 
handcraft each action description to fit correctly into 
the ~d hoe semantics of the specific plan for which the 
action is intended. As the number of operators for such 
systems increases it becomes impractical for their design- 
ers to maintain their consistency. 
To overcome these limitations, we argue that a dis- 
course planning algorithm should be formally sound and 
complete (or at least be formally characterizable in terms 
of these properties). While these formal characteristics 
may need to be relaxed in order to construct a planner 
for any given application, it is important to determine 
exactly bow a planning algorithm fails to meet these re- 
quirements. Without such a characterization one cannot 
specify what class of plans, and thus what class of dis- 
courses, are generated by a discourse planning system. 
In this paper we provide a general characterization 
of previous discourse planning systems in terms of these 
properties. We then describe a new discourse planning 
algorithm that extends recent work on partial-order, 
causal link (POCL) planning systems to represent hi- 
erarchical discourse plans. We show how this algorithm, 
called DPOCL (Decompositional POCL), provides a for- 
mal and explicit model of intentional and informational 
structure in its plans. In addition, we discuss DPOCL's 
formal properties. 
2 Representation in Discourse Plans 
Previous approaches have viewed the discourse planner 
as a means to producing a specification of a discourse 
that can be given to a text realization system in order 
to produce a series of sentences in a natural language. 
Recent work has shown that plans play a much larger 
role in agent interaction \[16\]. In particular, the structure 
of discourse plans plays a role in the comprehension of 
the discourse \[6, 11, 16\] and contributes to the nature of 
subsequent communication \[15, 24\]. 
2.1 Representing Intentional Structure 
As has been noted \[15, 16, 24\], a precise definition of 
intention in discourse plans is crucial for enabling sys- 
tems to respond appropiiately to failures of their com- 
municative actions. When a hearer reveals that an in- 
13 
7th International Generation Workshop * Kennebunkport, Maine * June 21-24, 1994 
tended effect of a previous discourse did not succeed, 
the speaker should re-try to achieve that effect. If, how- 
ever, the effect that failed was not an intended effect, 
the speaker need not generate an alternative response 
to achieve it. Alternatively, if the effect that failed was 
intended, but served only as a precondition of an action 
whose intended effects succeeded despite the failure, then 
again the speaker may chose not to respond. Clearly, dif- 
ferentiating between intended and unintended effects of 
discourse actions is critical for generating appropriate 
responses. 
As Maybury has pointed out \[13\], a realistic descrip- 
tion of communicative action requires a representation 
that allows individual actions to have more than one ef- 
fect on the mental state of the hearer. In particular, 
abstract communicative actions need to be described in 
a way that represents at least some of the effects of the 
steps in their subplans. Allowing action descriptions that 
have multiple effects complicates the definition of inten- 
tional structure. The reason for inserting a step in a 
plan is to establish some intended condition(s). How- 
ever, when steps have multiple effects, it may be the 
case that only some of these effects are used to establish 
intended conditions in any given context. Any effects 
of a step that do not play a role in establishing such 
conditions in a given plan are considered side effects. 
In Section 6.1, we formally define intention in the 
DPOCL framework, and show how intended effects are 
distinguished from side effects when action descriptions 
may have multiple effects. 
2.2 Representing Informational Structure 
Just as the structure of a discourse reflects the intentions 
of the speaker, that structure also reflects the way in 
which domain content is used to achieve intended effects. 
This informational structure captures relationships that 
hold between objects in the domain of discourse. In an 
explanation, for example, one utterance may describe an 
event that can be presumed to be the cause of another 
event described in the subsequent utterance. 
Clearly intention and information are closely related. 
An important component of an agent's linguistic caps- 
bility is the knowledge of what types of information can 
be used to achieve communicative intentions. Hearers 
may be able to determine what the speaker is trying to 
do because of what the hearer knows about the world or 
what she knows about what the speaker believes about 
the world. Alternatively, the hearer may be able to fig- 
ure out what the speaker believes about the world by 
recognizing what the speaker is trying to do in the dis- 
course \[16\]. In Section 6.1, we describe how decom- 
position operators in DPOCL capture the relationship 
between intentional and informational structure. 
3 Desiderata for Planning Algorithms 
A formal characterization of the capabilities of discourse 
planning algorithms is essential to understanding their 
\]imitations and is necessary before one can make claims 
about the kinds of discourse plans those planners can 
produce. 
3.1 Completeness 
The planning process of a generative planner is typically 
viewed as s search through the space of possible plans 
to locate a solution for a given planning problem. For 
some planning problems, no solutions exist. For others, 
many solutions can be constructed. A general-purpose 
discourse planner cannot anticipate the structure of the 
solutions to every problem. In order for these planners to 
be useful, they must be able to construct all solutions. 
Planners that are guaranteed to find all solutions to s 
planning problem are complete. 
Suppose there is a class of solutions to a planning 
problem that a discourse planning algorithm cannot find. 
It may be the case that the most appropriate solutions 
to the problem fall entirely into this class. If this hap- 
pens, the planner will only be able to construct the less- 
desirable plans. It may also be the case that the oni~ so- 
lutions to a planning problem fall into this class. If this 
happens, the planner will unnecessarily report failure. 
Consider those discourses in which individual utterances 
play several roles. Maier \[12\] describes the need for a 
system to generate this type of discourse and Hobbs \[7\] 
provides an example of one such discourse. We provide 
another example here: 
Lucentio has asked for Bianca's hand. He always 
considered her Senior Baptista's fairest daughter. 
That is also why Lucentio always chose her to model 
for his paintings. 
Here the second sentence provides support for the 
hearer's acceptance of both surrounding sentences. 
In a discourse planning model, these multi-role utter- 
ances correspond to actions that axe part of subplans 
for two different parent actions. That is, the plans that 
represent these actions are structured as directed acyclic 
graphs (DAGS) rather than trees. Planning algorithms 
that are incomplete because they can only produce tree- 
structured plans are not able to generate plans for this 
class of discourse. For the example above, these types 
of planners would produce less appropriate plans where 
the second sentence appeared twice as support in two 
distinct subtrees. 
3.2 Soundness 
Any system that plans before it acts assumes that its 
model of action is a useful one. Given that a system is 
using such a model, the plans that it produces should 
at least be internally consistent. That is, these plans 
should not have steps that interfere with one another. 
Furthermore, the planner should continue to add steps 
to a plan until the model indicates that all the plan's 
goals have been accounted for. Planning algorithms that 
have these properties are called 8outed. 
Given a model of planning where actions are related 
both causally and decompositionally, a sound planning 
algorithm must consider two factors when constructing 
14 
7th International Generation Workshop * Kennebunkport, Maine * June 21-24, 1994 
plans. First, for every step in a plan, the planner must 
ensure that each precondition of that step will be true 
just prior to its execution \[3\]. Second, the planner must 
consider the manner in which the steps of a subplan 
achieve the goals of the parent \[23, 25\]. While a par- 
ent step specifies the effects it has on the mental state of 
the hearer, it is the responsibility of the executable steps 
at the leaves of the subplan rooted at the parent step to 
ensure that those conditions are indeed established. 
Note that the soundness of a planning algorithm does 
not guarantee the success of the plans it produces. How- 
ever, the information about causal and decompositional 
relationships recorded in sound plans is crucial for de- 
termining where the planning model is in error and how 
to replan when an execution failure occurs. 
4 Previous Discourse Planning Systems 
Most current discourse planners (e.g., \[1, 2, 9, 13, 15\]) are 
based on the original NOAH model of hierarchical plan- 
ning. They rely on customized planning algorithms with 
pr0cedurai semantics for the purposes of solving specific 
text-planning problems, and thus their representations 
and algorithms suffer from being unprincipled and dif- 
ficult to analyze. Although these systems have resulted 
in successful text generation for specific domains and 
text types, careful analysis of these programs shows that 
there is nothing in their semantics to prevent them from 
generating incorrect plans, generating plans with redun- 
dant steps, or failing to find plans in situations where 
they exist. 
As Hovy et ai. \[8\] point out, these problems stem from 
an approach to discourse planning that does not clearly 
distinguish between the representation of communicative 
action and the design of a planning algorithm that ma- 
nipulates that representation. In most previous work, 
there has been no clear separation between the knowl- 
edge about the preconditions and effects of communica- 
tive acts and the knowledge about planning used to con- 
struct discourse plans. To the extent that these planners 
have been able to avoid generating incorrect or redun- 
dant plans, they have done so by severely limiting the 
expressive power of action descriptions and/or requir- 
ing the designer of action descriptions to handcraft each 
description to fit correctly into the ad/~oc semantics of 
the specific plan for which the action is intended. As 
Hovy et al. describe, when the number of operators for 
such systems increases, it becomes impractical for their 
designers to maintain their consistency. 
4.1 Representation of Discourse Plans 
Plans produced by most previous discourse planners 
have not adequately represented both the causal and de- 
compositional relations between actions in a discourse 
plan. As a result, their plans do not represent the man- 
ner in which preconditions are established, and, in cases 
where they represent action decomposition, the plans do 
not capture the relationship between the effects of ac- 
tions in a subplan and the effects of their parent action. 
Furthermore, they do not represent intentional and in- 
cl c4 ¢2 PAR~NT-~CnON 
c3 ~ c5 
r" '1 i 
ACTION1 ACTION2 ACTION3 
c7 c9 
el0 
ell 
Figure I: Schematic Discourse Plan Illustrating Par- 
ent/Subplan Effects 
formational structure in a way that clearly distinguishes 
the two. As a result, the intentional and informational 
structures in their plans are di~cult to analyze. Fur- 
thermore, the discourse operators for these systems lack 
the generality that would come from separating the two 
structures. 
Intention To illustrate these problems, consider the 
discourse plan shown schematicaily in Figure 1. 1 This 
plan has a structure that is typical of those produced 
by most previous discourse planning systems \[2, 9, 13, 
15\]. In this plan there is no explicit connection between 
the effects established by the parent action (c4 and c5) 
and those established by its subplan (c6, c7, c9, el0, 
el2 and c13). Previous approaches only represent the 
relationship between actioas at different levels; they fail 
to capture the relationship between the effects of those 
actions. In Figure 1, the top-level goal is cdAc5. Suppose 
that c6 unifies with c4, and that c9, el0, and c12 together 
have a consequence that unifies with c5. In this case, c7 
and c13 are side effects of choosing the decomposition of 
the PARENT-ACTION into ACTION1, ACTION2 and 
ACTION3. This fact, however, is not captured in the 
discourse plan of Figure 1. A system relying on this plan 
could not distinguish intended effects from side effects, 
and so would be unable to determine that a different 
response is warranted when c6 fails than when c7 falls. 
In addition, there is no explicit representation of the 
relationship between two steps when one establishes a 
precondition for another. In Figure 1, ACTION3 has 
ell as a precondition. Suppose that both el0 and c7 
unify with ell. If el0 falls it is possible that c7 will 
serve to establish the condition needed by ell. With- 
out a representation of the causal roles that these effects 
play, a system cannot determine whether an additional 
response is required. 
Informat|onal Structure in Prev|ous Systems 
Most previous planning systems do not provide an ex- 
plicit representation for either intentional or informa- 
tional structure. As noted in Hovy, et al \[8\], to the extent 
that informational constraints were represented, each set 
of constraints was duplicated for many similar discourse 
tin this plan, the dashed arcs indicate the decomposition 
of PARENT-ACTION into the actions in its subplan. The 
ci's represent conditions in the world - those to the left of an 
action are the action's preconditions and those to the right 
of an action are its effects. 
15 
7th International Generation Workshop * Kennebunkport, Maine • June 21-24, 1994 
operators. Many of these operators differed only in their 
intentional structure. As described in \[16\], combiningin- 
tentional and informational representations in this way 
can result in a proliferation of operators. Every inten- 
tional structure must be paired with every informational 
one, possibly requiring as many as n × m operators for 
domains with n intentional and m informational struc- 
tures. 
4.2 Computational Properties 
While previous discourse planners have been successful 
at generating appropriately structured plans for specific 
domains, these systems have ignored the analysis of the 
formal properties of the planning algorithms that pro- 
duce them. As has been noted in \[20, 3\], NOAH, and 
consequently those discourse planners based on it, use 
ad hoc procedures for the construction of plans. As a re- 
sult, the formal properties of these planning algorithms 
are difficult to characterize. While a complete analy- 
sis of the planning algorithms used by previous systems 
\[2, 9, 13, 15\] is beyond the scope of this paper, several 
properties of these algorithms are straightforward to de- 
scribe. 
First, these planners do not guarantee that a step's 
preconditions hold prior to the step's execution and thus 
they are not sound. Furthermore, there is no relation- 
ship in any oftbese planners between the effects of parent 
actions and their subplans - planning to achieve an ef- 
fect at one level of abstraction does not guarantee that 
the effect is realized by any combination of executable 
actions. 
Second, these planners are not complete. While there 
may be many classes of plans that these systems cannot 
generate, their incompleteness can easily be seen when 
considering two factors. First, all of these systems use 
tree-structured plan representations. As a result, they 
cannot produce discourse plans where individual com- 
ponents play a role in more than one subplan. Second, 
most current discourse planning systems restrict steps in 
subplans to be totally ordered with respect to one an- 
other. For total-order planners to be complete they must 
be able to construct every possible step ordering. 
While the sacrifice of formal properties may be nec- 
essary for constructing an efficient implementation, it is 
important to characterize the conditions under which a 
planning system falls short of soundness or completeness. 
By characterizing the soundness of a planner two things 
become apparent. First, the conditions under which a 
planner will introduce flaws into a plan are completely 
characterized. Second, the nature of the flaws that might 
be introduced under those conditions are specified. Sim- 
ilarly, characterizing the completeness of a planner spec- 
ifies the classes of plans that can and cannot be pro- 
duced by a planner. Without an understanding of these 
properties for a given algorithm, it is impossible fully to 
evaluate its usefulness for a particular application. 
5 The DPOCL Discourse Planner 
The DPOCL discourse planner is an extension to recent 
partial-order causal link planners \[14, 17\]. In POCL 
planners, a plan is represented as a set of partially- 
ordered steps connected by causal links. Two steps in 
a plan are connected by a causal link when the effect of 
the first step is used to establish the precondition of the 
second step. Steps and corresponding links are added 
to the plan to establish unsatisfied preconditions, and 
additional constraints are placed on the plan only when 
needed to maintain consistency. Previous POCL plan- 
ners have been non-hierarchical; DPOCL provides an ex- 
tension that introduces action decomposition into the 
POCL framework. For a complete definition of DPOCL 
see \[25\]. 
In the following discussion we will refer to the sample 
discourse from Section 3.1. Figure 2 shows an example 
of a DPOCL plan structure for this discourse. Consider 
• the subplan for Support~modeled(L,B)), rooted at the 
step marked as step ~1. ~ A decomposition link (shown 
using dashed arcs) connects Support(modeled(L,B)) to 
the begin and end-subplan steps bounding its subplan. 3 
This subplan is made up of the two Cause-to-Believe 
steps and the Combine-Belief step shown in between the 
begin-subplan and end-subplan. A causal link (shown 
using a solid arc and labeled with the effect that it con- 
tributes) connects Cause-to-Believe(falrest(L,B)) to the 
End-Subplan step. 
The manner in which a hearer combines the informa- 
tion in an utterance with his prior beliefs is critical to 
the generation of the utterance. Most previous work has 
made use of highly simple models of this process: for 
instance, it has assumed that the effect of asserting a 
proposition p is that the hearer believes p. In fact, a 
speaker may go to great lengths to convince the hearer 
of the truth of a proposition. She may first assert it, then 
support it, and then provide support for the intermedi- 
ate statement. In such a case, the speaker presumably 
believes that the combination of utterances is what leads 
the hearer to accept the main proposition. 
This phenomenon is represented by the Combine- 
Belief(z'~ action, where ~ is a vector of relevant beliefs. 
This Combine-Belief action provides an abstract model 
for an action taken by the hearer rather than by the 
speaker. A complete model of the manner in which a 
user combines belief from several utterances is beyond 
the scope of this paper. Further formal work in this area, 
such as that in \[I0\] is essential for accurately represent- 
ing the structure of discourse plans. DPOCL provides a 
2 Subplans in this figure are g~ouped inside rounded boxes 
for ease of reference. 
3DPOCL uses the standard POCL technique of encoding 
the initial conditions and the goals of a planning problem 
as the effects of a null initial action and the preconditions 
of a null final action, respectively. Simi|arly, every subplan 
is bounded by a null stsxt-subplan and a null end-subplan. 
Each staxt-subplan has as its dfects the preconditions of its 
parent action, and each end-subplan has as its preconditions 
the dfects of its parent action. 
16 
7th International Generation Workshop • Kennebunkport, Maine * June 2124, 1994 
ileilin-Pian r; 
~ei~-Subpl~m 
L 
cause-lo-i~li~ve C~01xr~dO.ji))) -- 
L=. 
i~f~m(pmlx~d(LJl))) 
LucentIo has asked for ~.Bel(propolled(Lji))) 
Bianca's hand in marriage. Combine-Be.lid(x) 
S~plxxi(p~Ji))) 
r .... ° 
I 
I ¥ 
i~gin-Subpl~ 
t 
ilei(pmpc~d(L,B))) 
EndoHan 
C~io-Belive(~xlel~0.,jl))) 
,t 
:~ Emd~ 
Sei(ir0po~i(L,S))) I 
fr ................................... -----=,,,. _-- '\], i ~ That is why he always asked ~, v her to model for his liInt#~ll ~ ~# 
Bellin-Sabplan Combine-Belief(x) ~ End-Subpllm 
Bel(model~l(L,B))/ Bel(modeled(L,S))) 
i i i ~um(firesi(l.Jl), pmferred(L, ll)) i 
llei(pmix~diL, S))) ~d-Subp~n / r - '" ............... 
Combine-Belief(x) ~ , / I i I I / I I caus~s(faizest(L,B),model~l(L,B)) I I 
k / /i ' ' i 
- /v v \ ~_ ~ Combine-Belief(x) ......... 
\ B~in-Subplan ~ ~ 'r"m"'~""Jl"~ 
x I Bei(~xld~tZ.,B))) n c ".Z->" J 
~- CJnse-to-Believe(fairesi(L,B))) 
I 
f l i r t 
ilegin-Subplan ief0rm(fairesi(LJi))) ~ ~i-Subpl~m 
He considers her the fairest J 
of Signior Baptism's daughten~ 
Figure 2: A Complete Discourse Plan 
framework for incorporating these approaches. 
5.1 Representation in DPOCL 
The representation of each action in DPOCL is separated 
into two parts corresponding to the causal and decompo- 
sitional roles the action plays: the action operator, and 
a set of decomposition operators. The action operator 
captures the action's preconditions and effects, sets of 
first-order unquantified sentences similar to the typical 
precondition and addldelete lists of STRIPS \[5\]. Each 
decomposition operator represents a slngle-layer expan- 
sion of a composite step, essentially providing a partial 
specification for the subplan that achieves the parent 
step's effects given its preconditions. In addition to spec- 
ifying the steps in the subplan, the decomposition opera- 
tor specifies any variable binding and temporal ordering 
constraints between the steps, and the causal links be- 
tween steps of the subplan that enable them to establish 
the parent step's effects. 
Figure 3 shows the action operator and one decom- 
position operator for the Support act. 4 As we see in 
the action operator in this figure, Support(?prop) has 
the effect of increasing the belief in proposition ?prop 
for the hearer. The decomposition operator in Figure 3 
was responsible for expanding the Support labeled #I in 
'The operators in this t~gure are shown wlth some detail 
omitted for clarity. 
17 
7th International Generation Workshop • Kennebunkport, Maine • June 21-24, 1994 
Action 
Header: 
Preconditions: 
Effects: 
Bindinss: 
Deco~po#itio~ 
Header: 
Constraints: 
Steps: 
Links: 
Bindinss: 
Orderings: 
Support(?prop) 
not(Believe(?prop)) 
Bel(?prnp) 
~O~e 
Support(?propl) 
canses(?prop2, ?prop1) 
Start, Cause-to-Believe- 1(?prop2 ) 
Canse-to-Believe-2(csuses(?prnp2,?prnpl)) 
Comblne-Belief, Final 
<Combine-BelieS ?prop, ?prop, Final> 
Figure 3: Support Action and Decomposition Operators 
Figure 2. The subplan specified by this decomposition 
has three steps in its body: two Cause-to-Believe actions 
and a combination of belief by the hearer to strengthen 
her belief in ?prop. The constraints placed on this de- 
composition restrict the propositions used in the Cause- 
to-Believe steps to be ones that cause the proposition 
being supported. Dec0mpositional constraints are dis- 
cussed further in Section 6.1 
This decomposition operator is only a partial specifi- 
cation of the subplan for the Support step. In DPOCL, 
when a subplan is only partially specified, the planner 
is free to complete the subplan by using steps already 
appearing in the plan. In this way, DPOCL can avoid 
generating plans with redundant communicative actions. 
5.2 Overview of the DPOCL Algorithm 
In DPOCL, the process of cresting a completed plan 
involves iterating through a loop that chooses between 
refining the current plan decompositionaUy or refining 
the plan cansaily and then modifying the plan to ensure 
that the refinement has not introduced any errors. Fig- 
ure 4 summarizes the DPOCL planning algorithm. For 
a complete definition, see \[25\]. 
Causal refinement in DPOCL is essentially identical 
to causal refinement in previous POCL planners. An 
unsatisfied precondition of some step in the plan is se- 
lected and a causal link is added to establish the needed 
condition. Decompositional refinement essentially cre- 
ates a subplan for some composite action and adds the 
subplan to the plan. First, a decomposition operator for 
the chosen step is selected and the steps indicated in the 
operator are added to the plan. These steps are created 
in one of two ways. In the first case, a step is created by 
selecting an action operator of the correct ~ction type 
and instantiating s new step just as is done when a new 
step is added during causal refinement. In the second 
case, a step is added to the subplan by finding a step of 
the correct action type that already exists in the plan 
and using that step in the appropriate place in the new 
subplan. 
The DPOCL algorithm ensures that a subplan's ac- 
tions establish the effects of the parent action in a 
straightforward manner. The preconditions of a sub- 
Termination: If the plan is inconsistent, then backtrack. Otherwise, 
remove unused step and return the plan. 
Plan Refinement; Non-detenninlstlcally do one of the following: 
I. Causal Planning: 
(a) Goal Selection: Nondeterminlstlcally select a goal. 
(b) Operator Selection: Add a step to the plan that adds an effect 
that can be unified with the goal (either by instantiating the 
step from the operator library or by finding a step already in 
the plan). H no such step exists, bac.ktrack. Otherwise, add 
the binding constraints required for the conditions to unify, an 
ordering constraint that orders the new step before the goal step 
and add the causal llnk between the two. 
2. Decompositional Planning: 
(a) Action Selection: Nondeterministlcally select some unex- 
punded composite step in the plan. 
(b) Decomposition Selection: Nondetermlnlstic2dly chose an ap- 
propriate decomposition schema for this action whose constraints 
axe satisfied. Add the steps and subplaa components of the de- 
composition schema to the plan and update the llst of decompo- 
sition links to indicate the new subplan. 
Threat Resolution: Find any step that might threaten to undo any 
causal link. For every such step, nondetermlaisticai\]y do one of the 
following: . 
s Promotion: If possible, move the threatened steps to occur before 
the threat in the plan. 
s Demotion: If possible, move the threatened steps to occur ufter 
the threat in the plan. 
s Separation: If possible, add binding constraints on the steps in- 
volved so that no conflict can srlse. 
Recursive Invocation: Call the planner recursively with the new 
plan structure. 
Figure 4: DPOCL Planning Algorithm 
plan's final step are an copy of the effects of the sub- 
plan's parent step. The DPOCL planner will attempt 
to achieve them through causal refinement just as it 
achieves all other unsatisfied preconditions. In this way 
we guarantee that the effects of every composite action 
are achieved by the steps in its subplan. Furthermore, 
the exact relationship between the actions in a subplan 
and the establishment of those effects is made explicit 
in the causal links establishing those conditions in the 
subplan. 
As a result of adding steps to a plan, newly created 
steps may introduce threats to existing causal links. A 
step, $6, threatens a causal link between two steps Sb and 
Sc when 56 might occur between Sb and Sc and one of 
S~'s effects might undo the condition established in the 
causal link. To ensure that no causal links are undone 
by plan refinement, each threat in s plan is eliminated 
before planning proceeds. This is done either by order- 
ing the steps so that the threatening step cannot occur 
between the two causally-linked steps or by restricting 
the variable bindings of the steps to eliminate harmful 
interactions. 
6 DPOCL's Properties 
Plan structures in DPOCL represent three critical com- 
ponents. First, every causal connection between some 
18 
7th International Generation Workshop • Kennebunkport, Maine • June 21-24, 1994 
Definition 1 (Intended Effect) Let s be some step in a 
plan and e, be an effect of s. Effect e, is intended precisely 
~uhen at least one of the follozuing conditions holds: 
• There is some causal link from • to the final step of the 
plan such that e, establishes one of the goals of the plan. 
• There is some causal link from s to some step s! where s! 
is the final step of a subplan for a parent action sp such 
that 
- e, establishes one of the goals of the subplan (that is, a 
precondition of s ! ) and 
- the corresponding effect e,~ of sp is intended. 
• There is some causal link from s to another step s' such 
that 
- e, establishes one of the preconditiorts of s' and 
- some effect e,, of s' is intended. 
Figure 5: Intention in DPOCL 
step's effect and another step's precondition that relies 
upon it is marked by a causal link. Second, the connec- 
tion between the effects of every abstract action and the 
substeps that achieve those effects are marked by a com- 
bination of causal and decompositional links. Finally, 
the constraints restricting the applicability of decompo- 
sition operators are noted for every abstract step expan- 
sion. By providing an explicit representation for each 
of these components an adequate characterization of the 
intentional and informational structure of the discourse 
can be made. 
6.1 DPOCL's Representational Properties 
A Principled Representation of Intention The 
forrnalrepresentation of causal and decompositional con- 
nections between steps in the DPOCL plan makes the 
definition of intention in terms of these concepts straight- 
forward. Informally, an effect is intended if it plays a 
causal role in the plan. That is, if it is used in a causal 
link and the step that asserts that effect is connected by 
that causal link through subsequent causal and decom- 
positional links ultimately to the final step of the plan. 
The formal definition of an intended effect is shown in 
Figure 5. 
Although the plan shown in Figure 2 does not ex- 
plicitly illustrate how our representation addresses cases 
where action descriptions have multiple effects and so 
distinguishes between intended and side-effects in the 
same action, our model handles these cases appropri- 
ately. Our solution rests on the fact that our model 
makes a clear distinction between effects of discourse ac- 
tions that play a role in achieving the top-level goals of 
the discourse plan and effects that are not causally linked 
in a way that contributes to the agent's ultimate goals. 
An Explicit Representation of Informational 
Structure Decomposition operators in DPOCL en- 
able us to represent the knowledge speakers have about 
how to use domain information to achieve communica- 
tive intentions. For example, one way for a speaker to 
increase a bearer's belief in a proposition (i.e., to sup- 
port a proposition) is to describe a plausible cause of 
that proposition. In DPOCL, we represent this "rule 
of language" using a decomposition operator as illus- 
trated by the decomposition operator in Figure 3. This 
operator says that one way to support a proposition 
?prop1 is to find another proposition, ?peop2, such that 
causes(?prop2, ?propl) is true in the domain. If such 
a ?prop2 can be found, then the speaker can support 
?propl by making the hearer believe ?prop2 and the re- 
lation causes(?prop2, ?prop1) In this way, information 
in the domain acts to constrain what language rules are 
appropriate and, given any particular rule, what objects 
can be referred to when it is used. 
The representation of the informational structure in 
a DPOCL plan is straightforward. Each decomposi- 
tion operator in DPOCL lists the informational con- 
straints that must hold in order for an abstract action 
to be achieved by the subplan dsfined in that opera- 
tor. During plan generation, informational constraints 
are checked for consistency whenever a modification is 
made to the plan and backtracking occurs when a con- 
straint is violated. In addition, these constraints are 
explicitly recorded in the plan data structure. The in- 
formational structure is made available to the realization 
component that is responsible for transforming the dis- 
course plan into a series of natural language utterances. 
6.2 DPOCL's Computational Properties 
Because DPOCL is built upon well-understood POCL 
planning algorithms, DPOCL inherits many of these 
algorithms' formal properties. Specifically, DPOCL is 
both sound and, for certain classes of plans, complete. 
Proofs of soundness and completeness can be found 
in \[25\]. With respect to the class of plans that DPOCL 
can generate, DPOCL is primitive complete. That is, 
it can generate sl1 possible sequences of executable ac- 
tions, but not necessarily all hierarchical structures that 
could account for those executable actions. In par- 
ticular, DPOCL cannot generate plans where two ab- 
stract steps are ordered one before the other in order to 
avoid a harmful interaction but some interleaving of the 
steps in their subplans exists that avoids this interac- 
tion. For a more complete description of this restriction 
on DPOCL's completeness, see \[25\]. 
7' Discussion 
As others have pointed out, the precise representation 
of intentional and informational structure is critical to 
the effective use of discourse plans. In addition, we have 
argued that a formal characterization of the planners 
that produce those plans is essential to evaluating their 
usefulness for any given: domain. As we discussed in 
Section 4, while previous work addressed some of these 
19 
7th International Generation Workshop • Kennebunkport, Maine • June 21-24, 1994 
issues, their approaches did not resolve the problems we 
have identified. 
In contrast, the DPOCL planner provides an explicit 
and formal representation of the intentional and infor- 
mational structures in its discourse plans. This model 
clearly differentiates between intended and unintended 
effects, allowing appropriate responses to discourse fail- 
ure. In addition, the information constraining each de- 
composition is formally represented as constraints on the 
applicability of the decomposition operator. The repre- 
sentation of these constraints is independent from any 
particular intentional structure formed by the subplan 
they constrain. 
Furthermore, the DPOCL planner builds upon a clear 
and precise formalism that allows the algorithm to be 
completely characterized. Specifically, DPOCL is sound 
and, for some class of plans, complete. It is precisely this 
formal analysis that allows us to specify exactly what 
class of plans DPOCL cannot generate. This analysis has 
not been performed for previous discourse systems and 
so they cannot similarly characterize their algorithms. 
8 Acknowledgements 
The authors would like to thank the anonymous reviewers for 
their helpful comments. The research described in this pa- 
per was supported by the Office of Naval Research Cognitive 
mad Neural Sciences Division (Graazt Number: N00014-91-J- 
1694). Young is supported by a grant from ON11 under the 
FY93 Augmentation of Awards for Science and Engineering 
Research Tr~in;~g (ASSERT) Program. 

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