Reasoning about Coherent and Cooperative

System Responses

?

Kristiina Jokinen

Centre for Computational Linguistics, UMIST

PO Box 88, Manchester M60 1QD

United Kingdom

??

Abstract. This paper discusses the planning of system responses in

information-seeking dialogues. Many dialogue systems are capable of an-

swering single questions or carrying out dialogues which have fairly �xed

structures, but they show little or no capability to continue the dialogue

in an intelligent way, if something unexpected takes place. Our system

aims to be exible and overcome the shortcomings in its knowledge base

by contextual reasoning that deals with the enablements and require-

ments for communication. Dialogue is regarded as a negotiation and the

most appropriate response in the context is determined by communica-

tive principles that are considered as constraints on cooperative and co-

herent communication. The prototype system is based on the knowledge

base update procedure developed by Guessoum and Lloyd (1990, 1991),

and it is a part of the Dialogue Manager in the PLUS system.

1 Introduction

This paper advocates the view that dialogue resembles a negotiation rather than

a straightforward question-answer sequence: the speakers push their own goals

and at the same time show understanding of the partner's goals. A similar view

is also found in Pollack et al. (1982) who studied naturally occurring user-expert

dialogues, and in Roulet (1986) and Moeshler (1989) who have developed a the-

oretical approach to dialogues in structural terms. Our work is inspired by ideas

about communication as a rational activity between rational agents as expressed

in Allwood (1976), and by dynamic interpretation in context as discussed in Bunt

(1990, 1991).

The discourse world is a dynamic construction, built and modi�ed while

the dialogue proceeds. Its coherence does not rely on any prede�ned dialogue

structure. Contributions are reactions to the partner's immediately previous con-

tribution, and they are locally planned and realised so that the communicative

?

This work was carried out as part of the PLUS-project, Pragmatics-Based Language

Understanding System, ESPRIT-II-project No. P5254. The �nancial support of the

CEC is gratefully acknowledged.

??

The author's current address is Nara Institute of Science and Technology, 8916-5

Takayama-cho, Ikoma-shi, Nara-ken, 6301-01 Japan.

requirements of the dialogue as a whole are respected. Following Allwood (1976),

coherence does not rely on any prede�ned classi�cation of dialogue acts, either.

Instead, communicative intentions are encoded in the expressive and evocative

attitudes associated with each contribution. The attitudes provide the basic

mechanism to deal with what the speaker intends to express and wishes to

evoke in the partner by making a particular contribution. They are based on the

speaker's cooperativeness and rationality, summarised in two general principles:

responsiveness (Allwood's concept that requires that the partner's contribution

be evaluated and the result of the evaluation be conveyed to the partner) and

minimalism (only the information that is new in the dialogue context must be

explicitly conveyed by the response). The contributions are linked together and

to the overall goal of the dialogue by the requirement that the expressive di-

mension of a contribution must match the evocative dimension of the previous

contribution.

Consider the following dialogue between a user and a system which provides

information from the Yellow Pages. The dialogue is a sample dialogue used in

the PLUS project.

USER1: I need a car.

SYSTEM1: Do you want to buy or rent one?

USER2: Rent.

SYSTEM2:Where?

USER3: In Bolton.

SYSTEM3: OK. Here are the car hire companies in Bolton:

<list of company names and addresses>

USER4: What is the cheapest car hire company?

SYSTEM4: Sorry. There is no information on prices.

Please contact the company.

USER5: Ok, thanks. Bye.

SYSTEM5: Thanks for calling. Bye.

Given that the main user goal is to get information and the main system goal

is to provide information, the dialogue shows how the two goals are satis�ed in a

cooperatively managed negotiation: both participants push their goals forward

by asking speci�cation or follow-up questions until they regard their goal ful�lled.

Moreover, the dialogue exempli�es some important aspects of human-computer

interaction to which a robust dialogue manager must attend:

1. The user can start with a vague request (USER1).

2. The system can re�ne the user question with respect to its own goal until it

matches some part of the information in the Yellow Pages database.

3. The user may continue with follow-up questions (USER4).

4. Helpful re-routing information is given when the system is unable to comply

with the request (SYSTEM4).

5. Both the user and the system contributions can be elliptical (USER2, SYS-

TEM2, USER3).

6. Anaphoric pronouns like `one' are used to refer to previous discourse refer-

ents, and thus to tie the discourse parts together (SYSTEM1).

7. A pragmatic marker `ok' is used to give feedback about the acceptance of

the previous response and topic change (SYSTEM3).

8. World knowledge is used to interpret the concepts and their relations: e.g.

that `needing' means `wanting to have'.

In this paper I will discuss problems (2)-(6), and describe how they can

be addressed in the PLUS framework. The point of departure for the studies

is the reasoning which takes place after the system goal and a possible plan to

achieve that goal have been abduced. This reasoning is called response planning.

I will not discuss the interpretation of a user input nor the goal formulation,

see Gallagher et al. (1992) for details. Surface generation which deals with the

translation of the semantic representation to a linear string of words is described

in Black and Cunningham (1992).

The structure of the paper is as follows. First the overall system design will

be described brie y. Then the response planning will be discussed in detail, and

�nally an example of the planning is presented, concentrating on the planning

of elliptical sentences, local dialogue management and follow-up questions.

2 Overview of the PLUS System

The PLUS project aims to study the use of context and pragmatics in human-

computer dialogues and to build a exible, co-operative interface to Yellow Pages

relying on pragmatic reasoning. Two views of dialogue management, the Query

Model and the Service Model, have been distinguished, and it is proposed that

the PLUS system accord with the requirements of the latter. The Query Model

approach is based on a simple question-answer pattern, where the user wants to

know something and the system is to supply the missing facts. The dialogues are

tailored to reach the correct answers in the most e�cient way, and the stepwise

speci�cation of the query mirrors the order in which the system knows that the

user goal can be satis�ed. The Service Model, on the other hand, concentrates

on the means of retrieving information. The user has a need or desire which the

system tries to satisfy by hypothesising a discourse world in which the object

of desire can be achieved. The system and the user gradually build a common

understanding of the current discourse world, about the user's needs and the

ways in which the system can best ful�l them. The hypothesised world being

constructed then shapes the dialogue understanding and helps in the analysis of

subsequent user queries as well as in the generation of system responses.

This can be compared with what Moore and Swartout (1990) argue about ex-

planation generation. The problem with existing generation systems is that they

view generation as a one-shot process whereby the system is supposed to give

the best and most appropriate response at once. However, explanation requires

a dialogue between advice-giver and advice-seeker: in naturally-occurring dia-

logues the advice-seekers do not necessarily state their queries clearly, and after

being given an answer, they quite often ask for elaborations and re-explanations

(Moore and Swartout call these follow-up questions). Moore and Swartout thus

claim that a more reactive system is needed: one that understands and monitors

the decisions and assumptions made, and with the help of the user's feedback

can alter its plans if necessary. This is in accordance with the goals of the PLUS

project, although the project concentrates on general pragmatic knowledge as

a constraint on communication rather than on the augmentation of rhetorical

relations with speaker intentions.

The PLUS system consists of the Natural Language Engine (parser and

surface generator), the Dialogue Manager and the knowledge bases. The Dia-

logue Manager comprises three principal subcomponents which are: the Cogni-

tive Analyser (CA), the Goal Formulator (GF) and the Response Planner (RP).

The CA deals with the parser output and produces the user goal, the GF is

responsible for �nding a relevant system goal, and the RP plans the appropri-

ate semantic representation of the goal for the surface generator to realise. This

paper concerns especially the functioning of RP. Figure 1 illustrates the internal

composition of the Dialogue Manager at a conceptual level.

RESPONSE

PLANNER

GOAL

FORMU-

LATOR

COGNITIVE

ANALYSER

CONTEXTUAL KBS:

Discourse Model

Pragmatic Rules

World Model

Yellow Pages db

DIALOGUE

MANAGER

-

�

FROM

PARSER

-

TO SURFACE

GENERATOR

�

Fig. 1. Dialogue Manager.

Reasoning is based on contextual knowledge bases. These comprise the Dis-

course Model which is dynamically built while the dialogue proceeds, and the

Pragmatic Rules and the World Model which are static KBs. The Yellow Pages

database is also considered part of the static context, since it encodes the knowl-

edge about the current application domain. The PLUS Discourse Model is de-

scribed in Jokinen et al. (1992), and the system's World Model and the Yellow

Pages database are described in Cavalli et al. (1992). Pragmatic rules encode

the system's knowledge about cooperative, rational behaviour as discussed in

Allwood and Bunt (1992), and a description of the rules can be found in Bego

et al. (1992).

PLUS proposes that abductive reasoning can be e�ectively used in hypothe-

sising appropriate responses. Abduction was �rst introduced by the philosopher

C.S. Peirce as one of the principal modes of human thinking, a way to hypothesise

explanations for observations: if A! B and B, we may infer A with some degree

of plausibility. In logic and natural language processing, abduction has recently

received more attention in connection with nonmonotonic and defeasible reason-

ing (see e.g. Hobbs et al. 1990; Norwig and Wilensky 1990; Appelt and Pollack

1991; Lascarides and Oberlander 1992). PLUS follows Gallagher and Guessoum

(1992) who argue that the knowledge base update procedure developed by Gues-

soum and Lloyd (1990, 1991) can be used in implementing abductive reasoning.

A detailed technical presentation of the Guessoum-Lloyd knowledge base up-

date procedure can be found in Guessoum and Lloyd (1990, 1991), below only

an outline is given.

The update procedure takes as its input a KB and a fact that is to be inserted

or deleted. It builds a SLDNF proof tree and outputs a set of transactions (a list

of Prolog assert/retract clauses) which would accommodate the requested fact

into the KB. Transactions can be understood as possible hypotheses to explain

the observed facts, and if the set is empty, this can be regarded as a proof of the

fact in the KB.

Gallagher and Guessoum (1992) claim that the knowledge base update pro-

cedure provides a basis for modelling various interactive systems, e.g. dialogues.

The dialogue context is encoded in the Discourse Model which is dynamically

modi�ed as the dialogue proceeds. Facts about the dialogue state will be added to

and removed from the Discourse Model Knowledge Base according to the prag-

matic rules that govern the reasoning process. Each user contribution changes

the Discourse Model, and the system tries to retain consistency of the knowledge

base by abducing possible explanations for the observed user input. The result

of this abductive reasoning is a hypothesised user goal. Transactions lead to the

formation of a system goal, which is abduced as a possible explanation of the

system's cooperative behaviour in its attempts to understand and ful�l the user

goal. A system response is then planned as a by-product of the system's attempt

to maintain the contextual knowledge base's consistency. The system's task is

completed when all the inconsistencies of the knowledge base are removed, and

a surface form generated to communicate the system goal to the user.

The uniform reasoning mechanism and inference regime leads to a radical

view of the Dialogue Manager as a whole: the distinction between the principle

subcomponents is not clearcut. All components have access to same data struc-

tures, the rules of the static KBs used in the interpretation of a user input and

the planning of a system response are the same, and the reasoning (updates and

inconsistency removal) deals with all knowledge available. It should be noticed,

however, that the uniform inference regime does not do away with the partition-

ing of the Dialogue Manager into distinctive subcomponents: pragmatic rules can

be grouped according to the knowledge that their left-hand side refers to, and

thus be indirectly ordered with respect to their application domain, i.e. di�erent

Dialogue Manager modules. In a large-scale system this kind of partitioning may

even be necessary to attain e�ciency in the system. Modularity of the system

is thus understood as a way to constrain search space, rather than a division

between functionally di�erent modules.

3 The Planning of Coherent Responses

3.1 Response Planner tasks

The task of the Response Planner is to plan a semantic representation for the

next system response, starting from the goal formulated by the Goal Formulator.

The RP operates on pragmatic knowledge and the general principles of respon-

siveness and minimalism ensure that the planning ful�lls the communicative

requirements of the negotiative dialogue: the goal will be realised in the context

so that it addresses the expectations raised by the previous dialogue, evokes a

reaction in the hearer that would support the ful�lment of the speaker's own

goal, and conveys no information that would lead the hearer astray.

In the PLUS system, the planning task contains four subtasks. The �rst two

implement the responsiveness principle and the last two implement the minimal-

ism principle.

1. Find a set of communicative functions that achieves the system goal and

which is consistent with the expectations from the previous contribution.

2. Decide on the thematic coherence of the contribution. The selected Topic

must be in accordance with the Topic Shifting rules or a marker must be

generated to make the awkward Topic Shift smooth.

3. Select the appropriate semantic predicates to realise the content of the goal.

This includes

(a) decisions about the realisation of communicative functions either as a sin-

gle sentence or multiple sentences, and in the latter case, decisions about

the rhetorical relation between the sentences (not fully implemented in

the prototype system),

(b) re�nement of the goal by selecting the concepts that are to be explicitly

communicated to the user (ellipsis generation),

(c) lexical lookup and disambiguation between alternative lexemes,

(d) determination of referring expressions on the basis of topic information.

4. Compute the expectations raised by the current contribution, and update

the dialogue context. However, most of the dialogue context updates are sup-

posed to be by-products of the reasoning, and hence no particular reasoning

task may be needed at all.

The completion of the di�erent subtasks results in a realisation of the sys-

tem goal as a conjunction of semantic predicates (a quasi-logical form or QLF-

representation) which, by de�nition, is a cooperative and thematically coherent

contribution in the dialogue context. The subtasks are performed in the above

order, and a failure in one task will lead to the replanning of the task in hand

or the previous one.

3.2 Response Planner Rules

A de�nition of pragmatic rule is as follows (from Bego et al. 1992):

A pragmatic rule is a rule whose antecedent refers to properties of the

Discourse Model, and whose consequent is either

{ a property of the Discourse Model, or

{ the predicate inconsistent.

Pragmatic rules can thus be divided into pragmatic constraints which deal

with the consistency of the knowledge base and whose consequent is the pred-

icate `inconsistent', and pragmatic rules proper which de�ne pragmatic actions

to manipulate the contextual knowledge bases.

The rules are not divided according to their function, but rather, according

to the contextual information that they encode. As the context is represented

in a declarative way, the same knowledge can be used in analysis and planning.

Pragmatic knowledge deals with

{ Intentions and beliefs of the interlocutors,

{ Expectations of dialogue situations,

{ Topic and New information,

{ Dialogue history,

{ Social roles and mutual relation of the interlocutors.

3

The knowledge about the participants' intentions, beliefs and expectations

is based on Allwood's concept of obligation. Given that the participants are en-

gaged in cooperative communication, their contributions put a certain reactive

pressure on the dialogue partner (a question typically puts a pressure on the

partner to provide an answer, for instance). The speakers are thus obliged to

follow particular constraints imposed by the previous contribution. The obli-

gations concern motivation (the speaker can support her response with facts

that she believes relevant in the context), consideration (the speaker attends to

the partner's need as a rational cooperative agent) and sincerity (the speaker is

truthful).

Obligations are formalised as pragmatic rules. For instance, the two moti-

vation rules in Fig. 2 state that everything that the system wants to know in

order to specify a user request, and everything that informs the user about an

inconsistency in the Discourse Model are motivated, and can be included in the

expectations of the planned contribution.

3

The reasoning about social aspects of communication (such as roles, attitudes and

relations between the partners) is not fully implemented in the current prototype

system.

wants(sys; knows(sys; X))

! wants(sys; wants(user; knows(sys; X)))

wants(sys; knows(user; inc(X)))

! wants(sys; wants(user; knows(user; inc(X))))

Fig. 2. Two pragmatic rules of motivation.

Figure 3 gives two examples of consideration rules: the �rst one states that

a compensation can be given if the required information is not found in the

database, and the second one that the system can repeat its previous goal, if the

system has the initiative and the NewInfo given by the user is not related to the

previous NewInfo.

YPQuery(X; nil)

! compensation(X)

initiative(sys);

DMCurrentContribution( Current);

DMPreviousContribution( Current; Previous);

DMNewInfo( Current; CurrNI);

DMNewInfo( Previous; PrevNI);

not related( CurrNI; PrevNI);

DMGoal( Previous; PrevGoal)

! repeat( PrevGoal)

Fig. 3. Two pragmatic rules of consideration.

Examples of pragmatic constraints are the 'Obligation Ful�llment Rule',

taken from Bego et al. (1992), and the 'Planner Control Constraint', both given

in Fig. 4. The 'Obligation Ful�llment Rule' says that the current contribution

should be subsumed by the expectations created by the previous contribution,

otherwise the dialogue state is inconsistent. The 'Planner Control Constraint'

ensures that the current contribution is realised as grammatical units, i.e. a dia-

logue state where a system goal exists without corresponding grammatical units

that would realise the goal is inconsistent.

The 'Obligation Ful�llment Rule' also exempli�es how the di�erent compo-

nents of the system have access to the same pragmatic knowledge. In inter-

pretation, the rule can be used abductively, by assuming that the current user

contribution is in fact subsumed by the expectations of the previous system con-

tribution, whereas in response planning, the rule acts as a constraint, forcing the

system to ful�ll the expectations from the previous user contribution.

Thematic knowledge deals with the Topic and NewInfo of the contributions.

An important distinction between foreground (topical) and background (salient)

DMCurrentContribution( Current);

DMPreviousContribution( Current; Previous);

DMContributionExpects( Previous; Expectation);

not subsumes( Expectation; Current)

! inconsistent

DMCurrentContribution( Current);

not DMGrammaticalUnit( Current; GramUnit)

! inconsistent

Fig. 4. Two pragmatic constraints.

information on the one hand, and between new (focal) and old (known) informa-

tion on the other hand is made (Jokinen et al. 1992). The Topic is a distinguished

discourse referent which is talked about, and the NewInfo is the new informa-

tion with regard to some Topic. The distinction is important in ranking discourse

referents according to their accessibility for reference: new and salient discourse

referents are the most accessible ones, then come old topics and �nally old salient

discourse referents.

To avoid terminological misunderstandings, the term Focus is not used. In

our terminology, NewInfo refers to the concept(s) that the system wants to com-

municate to the user and which form the basis for the planning of the semantic

representation. It is inferred as a part of the system goal formulation and thus

given as an input to the Response Planner. Topic, on the other hand, sets the

common ground for the speakers and constrains the view point from which the

NewInfo can be coherently talked about. An important task of the RP is thus

to hypothesise a Topic which would allow the system goal to be coherently re-

alised in the dialogue context. Since the NewInfo must be related to a Topic,

the context usually contains several candidates that are possible next topics,

i.e. there are several possibilities to continue the dialogue corresponding to dif-

ferent view-points from which the NewInfo can be looked at. However, the likely

next Topic must also be thematically related to the previous Topic so that it

either obeys Topic Shifting rules which describe smooth shifts or it is marked by

an explicit Topic shift marker which indicates an awkward Topic shift. Thus the

set of candidates is reduced to include only those that would form a coherent

continuation. If the set is not singleton, the ranking heuristics are used to select

an appropriate one.

Topic Shifting rules are formalised after McCoy and Cheng (1990). Smooth

Topic shifts are de�ned on the basis of World Model relations that are available

for for each type of concept. For instance, if the current Topic is of the type

event, a smooth Topic Shift can occur to an object which participates in the

event, to a super- or sub-event of the event or to the setting of the event. Some

constraints that deal with smooth shifts from an object-type Topic are listed

in Fig. 5. If the current topic candidate is of the type event, but the previous

object-type Topic does not participate in the event, the dialogue situation is

inconsistent. Similarly, if the current topic candidate is of the type Attribute,

but the previous Topic does not have the attribute, the dialogue situation is

inconsistent.

DMCurrentContribution( Current);

DMPreviousContribution( Current; Previous);

DMGetTopic( Current; CurrTopic);

DMGetTopic( Previous; PrevTopic);

DMTopicType( PrevTopic; Object);

DMTopicType( CurrTopic; Event);

not participates( Object; Event)

! inconsistent

DMCurrentContribution( Current);

DMPreviousContribution( Current; Previous);

DMGetTopic( Current; CurrTopic);

DMGetTopic( Previous; PrevTopic);

DMTopicType( PrevTopic; Object);

DMTopicType( CurrTopic; Attribute);

not attribute( Attribute; Object)

! inconsistent

Fig. 5. Pragmatic constraints on Topic Shifting.

An example of a pragmatic rule which deals with pronoun referents is given

in Fig 6. It says that a current Topic can be referred to by a pronoun, if the

Topic continues.

DMCurrentContribution( Current);

DMPreviousContribution( Current; Previous);

DMGetTopic( Previous; CurrTopic);

DMGetTopic( Current; CurrTopic);

! PronounReference( CurrTopic; Pronoun)

Fig. 6. Pronoun Reference Rule.

Planning of the surface realisation starts from the NewInfo, and thus an

important aspect of the process is the relevance of the concepts that are explicitly

included in the surface contribution. This concerns the intentions and beliefs that

would be expressed and evoked, if the concepts were a part of the utterance.

The reasoning about relevance must not be confused with the reasoning about

thematic relatedness: the former is de�ned with regard to the inferences that

the hearer can draw from the utterance as a whole, while the latter concerns the

central concept of the contribution with regard to its relations to other concepts

as de�ned in the World Model.

There are four relevance criteria, inspired by Reiter (1990) and modi�ed to

�t into the PLUS system. These are:

{ Accuracy (the utterance should be truthful)

4

: the contribution must ac-

curately represent the speaker's goal, i.e. at least the attitudes related to

NewInfo and Topic should be unambiguously communicated to the user,

{ Consistency (not included in Reiter (1990)): the set of attitudes that are to

be expressed and evoked by the contribution is consistent,

{ Validity (the utterance should trigger the desired inferences in the hearer):

the contribution must indicate that the hearer's intentions have been appro-

priately addressed in the dialogue context, i.e. expressive attitudes match

the hearer's evocative attitudes, and

{ Free from false implicatures (the utterance should not lead the hearer to

draw incorrect conversational implicatures): the contribution must not trig-

ger unwanted implicatures, i.e. the content must not evoke attitudes which

the speaker is not able to support.

The criterion of Consistency is added to prevent the system from planning

a contribution which would be internally contradictory. Its usefulness is con-

nected to the multifunctionality of the contributions. The criteria of Accuracy

and Consistency take care of the internal coherence of the contribution while the

criteria of Validity and FFI take care of the contribution's coherence in the dia-

logue context. The criteria of Accuracy and Validity guarantee that the correct

inferences are included in the set of communicated concepts, while the criteria

of Consistency and FFI guarantee that only the correct inferences are in the set.

3.3 Response Planner Algorithm

The four tasks discussed in Sect. 3.1 correspond to four major control steps

in the RP algorithm. The control structure of the Response Planner can be

partially encoded in the integrity constraints of the Contextual Knowledge Base.

For instance, the planning of a surface realisation is started by the planner's

attempt to remove the inconsistency of a state where a system goal exists but

an instance of grammatical units to realise the goal does not yet exist ('Planner

Control Constraint' given in Fig. 4), i.e. there is no semantic representation to be

forwarded to the Surface Generator. The RP Controller uses mainly pragmatic

rules proper to plan the semantic representation.

The Response Planner algorithm is sketched as follows:

1. Start the reasoning by removing inconsistencies in the knowledge base. The

�rst inconsistency concerns the system goal and its missing realisation. Dele-

tion of the system goal from the Discourse Model would lead to the negation

of the inferred user goal and eventually to the system's uncooperativeness,

4

The de�nition in the parenthesis corresponds to Reiter's de�nition.

and thus the chosen transaction is to hypothesise the grammatical unit in

the knowledge base. This will lead to a deductive check whether the insertion

is consistent with the knowledge base and how the grammatical unit can be

realised.

2. Decide on the appropriate \macro speech acts" Inform, WH-question and

YN-question. As the reasoning about the speaker's intentions and beliefs is

based on the expressive and evocative attitudes, the dialogue act determi-

nation can be reduced to deciding whether the speaker intends the hearer

to know something or intends to know something herself. The planning is

further simpli�ed by assuming that the macro acts will eventually determine

the surface form of the system response as a declarative or interrogative

sentence, respectively.

3. Instantiate Topic for the current contribution by abducing the best candidate

in the context on the basis of NewInfo, Goal, instantiated discourse referents

ranked according to their accessibility and Topic Shifting rules. The Topic

must be an instantiated discourse referent included in the Goal and related

to the NewInfo, and it must be thematically related to the previous Topic

according to the Topic Shifting rules. If no such candidate is available, fail.

The NewInfo cannot be coherently related to the current dialogue state, and

replanning of the system goal must take place. Control is returned back to

the Goal Formulator.

5

4. Select the semantic predicates which will realise the Goal. The concepts that

are to be explicitly communicated to the user are hold in an auxiliary data

structure called the Agenda. Initialise the Agenda by pushing the NewInfo

into it. If the NewInfo cannot be lexically realised, fail. The NewInfo cannot

be expressed by the system, and replanning of the system goal must take

place. Control is returned back to the Goal Formulator. If the concepts can be

lexically realised, they will be explicit on the surface contribution, otherwise

they must be implicitly communicated, i.e. included in the implications that

the hearer can draw from the explicit concepts. Check the appropriateness

of the hypothesised realisation by the abductive update procedure: insert

the system contribution in the context and produce the transactions that

are needed to maintain the consistency of the Contextual Knowledge Base.

These inferences represent the interpretation of the system contribution in

the current context and they must be in accordance with the four relevance

criteria. If not, add more relevant goal concepts to the Agenda and check if

the relevance criteria are ful�lled. Repeat until the corresponding semantic

5

This step is due to the passive nature of the PLUS system: it is assumed that the

system is only a simple information provider and its ability to introduce new topics

is restricted to those that are thematically related to the dialogue. If the system had

more freedom in taking initiatives, a brand new Topic could always be introduced by

adding a topic shift marker to the semantic representation, and thus the RP would

always accept the goals produced by the GF, assuming that the GF can provide

enough motivation for the contextual appropriateness of an awkward topic shift

(e.g. there is a need to discuss a previously ignored subtask in order to ful�l the

main task).

representation communicates the goal, is realisable and does not convey false

implicatures.

5. When the Agenda ful�ls the relevance criteria, output the corresponding

semantic representation to the Surface Generator. If pragmatic rules and

relevant concepts are exhausted but Agenda does not ful�l the relevant cri-

teria, fail. The system Goal with the assumed Topic cannot be realised. The

control is returned to the Topic instantiation, another Topic is hypothesised,

and the realisation starts again with the new Topic. If no Topic candidates

are available, the control is returned back to the Goal Formulator, and re-

planning of the system goal must take place.

4 Example

As an example we describe the system responses in the previously introduced

sample dialogue, repeated below for convenience.

USER1: I need a car.

SYSTEM1: Do you want to buy or rent one?

USER2: Rent.

SYSTEM2: Where?

USER3: In Bolton.

SYSTEM3: OK. Here are the car hire companies in Bolton:

<list of company names and addresses>

USER4: What is the cheapest car hire company?

SYSTEM4: Sorry. There is no information on prices.

Please contact the company.

USER5: Ok, thanks. Bye.

SYSTEM5: Thanks for calling. Bye.

The subsections will discuss how the elliptical and full sentences are planned,

how the dialogue is locally managed, and how the follow-up questions and com-

pensation are addressed.

4.1 Planning of elliptical and full sentences

We do not go into details of the reasoning that deals with recognition of the

user's plan or formulation of the system's goal, but start from the system goal

that is to be realised. For the �rst system contribution, the reasoning up to this

point can be summarized roughly as in Fig. 7.

Hence, the goal of system response SYSTEM1 is to know whether the user

requires information on car hire companies or car sale companies, and this is

encoded as an intention to know whether the user is buying or renting a car (1).

(1) wants(sys,knows(sys,or(Buying(_b,_,user,car1,_),

Hiring(_h,_,user,car1,_))))

need(user,car)

if not-have(user,car)

if wants-to-have(user,car)

if wants(sys,wants-to-have(user,car))

if provides(sys,user,car)

if provides(sys,user,info-on-cars)

if knows(sys,or(InfoOnHireCos,InfoOnSaleCos))

if knows(sys,or(Buying(_b,_,user,car1,_),

Hiring(_h,_,user,car1,_))).

Fig. 7. A Sketch of the reasoning in the analysis of the �rst user contribution.

The NewInfo conveyed by the goal is the choice between the two events Buying

and Hiring, i.e. the disjunction

or(Buying(_b,_,user,car1,_),Hiring(_h,_,user,car1,_)).

6

Because the grammatical units that would realise the current system goal

do not yet exist, the knowledge base integrity constraint 'Planner Control Con-

straint' (also given in Fig. 4) is violated.

DMCurrentContribution( Current);

not DMGrammaticalUnit( Current; GramUnit)

! inconsistent

This leads to two alternative transactions which would remove the inconsis-

tency, if executed: either the current contribution Current is retracted from

the KB, or the grammatical unit is inserted in the KB. The Response Plan-

ner has a cooperativeness meta-rule that prevents it from retracting an instance

from the dialogue history. In this case the retraction of Current would undo all

the previous hypotheses about the dialogue, and thus the only choice is to in-

sert the fact DMGrammaticalUnit( Current, GramUnit), which initiates further

planning steps.

The Topic is hypothesised on the basis of Topic-shifting rules. The Topic

belongs to the intersection of Goal Concepts and discourse referents instantiated

so far, and it must be related to the NewInfo and to the previous Topic (which

in this case is the instantiated concept BeCar). As the user has stated a need

for a car and the system wants to know what the user wants to do with a car,

the Topic continues smoothly and is chosen to be BeCar.

The planning of the semantic representation of a goal starts from the NewInfo

which must be explicitly expressed at least. It follows from the the Minimalism

Principle that the default contribution is an elliptical utterance of NewInfo alone,

and only if this representation does not convey all the intentions that are to be

6

We do not discuss the representation of the World Model concepts, but refer to

Cavalli et al. (1992) for details.

communicated, or does not address the expectations of the dialogue partner, will

the salient background be made explicit.

The Agenda now contains the NewInfo or the disjunction between buying

and hiring:

(2) or(Buying(_b,_,user,car1,_),Hiring(_h,_,user,car1,_))

This can be realised as a conjunction of semantic predicates as in (3) (only the

predicates that appear as conjuncts in the conjunction will be realised as word

forms; arguments of the predicates will be realised only, if there is a predicate

for them in the conjunction):

(3) question & or(or1,b1,h1) & buy(b1,user,car1) &

hire(h1,user,car1)

On the surface level, this would be realised as an elliptical expression Buy

or rent? However, the content of the Agenda as a whole must ful�l the four

relevance criteria. In this case, the Agenda is Accurate (it conveys the system

intentions), but it is not Valid: it fails to indicate that the user intentions have

been appropriately addressed in the context.

The evocative attitudes conveyed by the previous user contribution USER1

(I need a car) are the following:

(4) wants(user,knows(sys,Need(n1,user,car1)))

(5) wants(user,knows(sys,Want(w1,user,Have(h1,user,car1))))

The elliptical surface form would express only one attitude, namely the sys-

tem goal (1). Thus the form addresses neither the user's intention that she wants

the system to know that she needs a car (4), nor the inferred intention that she

wants to have a car (5). It leaves the hypothesised explanation of the user's plan

open, since it only expresses the system's intention, but not how this intention

is related to the user's wants. The system has reasoned that having a car means

either buying or renting a car, since this is the only information that the system

can �nd from its database to `explain' the user need. However, the system cannot

assume that this piece of information is implicitly communicated in the elliptical

contribution, since the user may not be familiar with the system's database and

its `explanations'.

To remedy the Validity of the system contribution, the concept of the user's

want is added to the Agenda. This concept is associated with the expressive

attitude that the system intends to know whether the user wants to buy or rent

a car. The revised set of intentions to be communicated to the user thus contains

the following two attitudes:

(6) wants(sys,knows(sys,or(Buying(_b,_,user,car1,_),

Hiring(_h,_,user,car1,_))))

(7) wants(sys,knows(sys,Want(w1,user,

or(Buying(_b,_,user,car1,_),

Hiring(_h,_,user,car1,_)))))

The semantic representation for the system contribution now becomes (8)

(we have omitted auxiliary predicates like pres time which are not important for

the point to be made here):

(8) question & want(w1,user,or1) & you(user) & or(or1,b1,h1) &

buy(b1,user,car1) & hire(h1,user,car1) & one(c,car1)

This representation corresponds to the Agenda which is Accurate, Valid,

Consistent and Free From False Implicatures, and it can be realised on the

surface level. Notice that the reference to the current Topic BeCar is made by a

pronoun according to the 'Pronoun Reference Rule' in Fig. 6. The choice of the

lexical item 'one' instead of 'it' is preferred as the Topic appears in the scope of

an intentional predicate 'need'.

The Surface Generator takes care of the correct question formation, and

the result will be Do you want to buy or rent one? Before control is handed

over to the Surface Generator, however, the Response Planner checks that the

Contextual Knowledge Base is consistent, and all the integrity constraints are

respected.

We now turn our attention to system response SYSTEM2 of the example

dialogue and discuss how an elliptical system response is planned. We can assume

that the elliptical user contribution Rent has been analysed as containing the

evocative attitude (9) (the user wants the system to know that the user wants to

rent a car). The reasoning process sketched in (1) can now be stepped further,

since the the disjunction can be resolved by inserting the attitude (10) (user

wants to get information on car hire companies) to the Discourse Model.

(9) wants(user,knows(sys,Want(w1,user,

Hiring(h1,_,user,car1,_))))

(10) wants(user,knows_val(user,infoOn(BeCarHireCompany,_info)))

The list of all car hire companies in the Yellow Pages database is long, but

the system can cut down the number of companies by restricting the search to

car hire companies in a particular location. The system has thus formulated the

goal (11), i.e. the system wants to know the value for the car hire companies'

location.

(11) wants(sys,knows_val(sys,

BeIn(_i,Hiring(h1,chco,user,car1,_),_Loc)))

The NewInfo is now the value of the location of the hire-event h1, represented

in the concept BeIn. The Topic is abduced to be the concept Hiring, since this

provides a smooth shift from the previous Topic BeCar (the shift is to an event

in which the previous object-type Topic participates, cf. Topic Shifting Rules

in Fig. 5). As before, the system �rst tries to realise the NewInfo alone. The

Agenda contains the concept BeIn(_i,Hiring(h1,chco,user,car1,_),_Loc),

which has the semantic representation

(12) question & where(h1)

This is Accurate since it conveys the system's intention to know the location of

the hire-event. It is also Valid, since it addresses the user's intention to get infor-

mation on car hiring. Although the user does not explicitly evoke her intention

to tell the system the location of the requested car hire companies, the user is

expected to behave in a cooperative manner and share the relevant information

with the system. As the user wants information on car hire companies, she ob-

viously also wants the system to know where the desired hire-event would take

place, i.e. the location of the car hire companies to be searched for. The system

can thus push its goal forward and rely on the cooperativeness of the user: the

shared knowledge and understanding of each other's goals obliges the user to

attend to the system's needs as well.

The Agenda is also Consistent and Free From False Implicatures, and thus no

other concepts need be explicitly realised. The planner regards the corresponding

semantic representation as relevant in the context, and the Surface Generator

will realise it as the response SYSTEM2: Where? No particular mechanism is

needed to drop 'unrelevant' concepts from the surface generation. Instead, el-

lipsis is regarded as the default realisation in accordance with the Minimalism

principle. Since the pragmatic rules do not require more concepts to be realised,

the elliptical utterance is accepted as a communicatively relevant contribution in

the dialogue context. Of course, linguistic information about the grammatical-

ity of the planned utterance also restricts ellipsis generation: e.g. a contribution

like Do want to buy or rent? is ungrammatical and thus discarded as a possible

system response SYSTEM1, although one can argue that it only contains new

information.

4.2 Local Dialogue Management

The system responses SYSTEM1 - SYSTEM2 have been examples of a straight-

forward local dialogue management: the system has reacted to the NewInfo of the

immediately previous user contribution and planned its responses with respect

to its own goal to provide the user with some appropriate amount of informa-

tion from its Yellow Pages database. In SYSTEM3, locality may not not be so

obvious, since the system seems to respond to the original user request about

car hire information. In structural terms, this is a point where the subdialogue

SYSTEM1 - USER3 ends, and the system reverts to the main exchange.

However, our system does not use structural information, but relies on the

local reasoning about the current context. The number of car hire companies

cannot be reduced further and thus the system goal to provide information can

be ful�lled. The system wants to inform the user of the information found, and

the goal is as follows:

(13) wants(sys,knows_val(user,infoOn(BeCarHireCompany,

[list-of-companies])))

The realisation of the goal (13) proceeds as above. The only Topic candidate

is now BeCarHireCompany which provides a smooth continuation from the pre-

vious Topic Hiring (it is a participant in the hire-event). However, it is not an

instantiated discourse referent and must thus be explicit in the response. Con-

sequently, an elliptical response which would only contain the NewInfo (the list

of car hire companies) is ruled out since it would be thematically incoherent.

7

Ellipsis is also excluded because such a contribution is not Valid in the con-

text. The system has previously inferred the user attitude (10) to explain how

the user goal is related to the system's own goal. However, as above with the

planning of the �rst system response and the inferred user attitude (5), the sys-

tem cannot assume that the user is familiar with this inference. The Agenda

must thus be augmented with the concept of BeCarHireCompany, and �nally

the system goal will be realised as the system response SYSTEM3.

The response is formulated and planned locally, according to the pragmatic

rules about thematically coherent and communicatively relevant response. The

system does not refer to structural concepts like 'exchange' or 'subdialogue', but

plans its response on the basis of facts in the current Contextual Knowledge Base.

This is regarded as one of the main advantages of local dialogue management:

the system's exibility is encoded in its ability to plan di�erent realisations of the

same goal, because its knowledge about the current dialogue situations di�ers,

not because it happens to store di�erent prede�ned schemas for some possible

abstract dialogue structures.

4.3 Follow-up questions and compensation

The last point to be made concerns the appropriate response to the user's follow-

up question USER4. As argued in Moore and Swartout (1990), such responses

must be based on the knowledge about what has been discussed and what is

assumed to be known by the user.

Again, we do not go into the analysis of the user question, but assume that

it has successfully resulted in a goal to know about prices of car hire companies.

Since the Topic of the previous contribution is BeCarHireCompany, the user

request is understood as referring to the companies just listed. If the system had

information about the prices, probably sorted according to their expensiveness,

this could be given to the user. Since the system does not have such information,

the negative result is compensated with the request to contact the company

directly (cf. the consideration rule for compensation in Fig. 3). The system has

now a goal with two intentions to be realised:

(14) wants(sys,knows_val(user,infoOn(Price,nil)))

(15) wants(sys,wants(user,Contact(c1,user,chco)))

The intentions are realised in this order and full sentences are generated for

both. Elliptical contribution with the NewInfo 'nil' is not Accurate since it does

7

Compare the sample dialogue with the following dialogue where the elliptical list is

�ne:

USER1: Give me a list of car hire companies in Bolton.

SYSTEM1: <list of company names and addresses>

not convey the whole goal (the intention (15) is missing). Moreover, the whole

compensation is NewInfo in the current context and must thus be explicitly

communicated.

The follow-up question is evaluated in the current dialogue context like any

other user contribution. This is a sign of the negotiative nature of the dialogue:

the fact that one dialogue partner (in this case: the system) has successfully

ful�lled his goal does not mean that the other dialogue partner (in this case: the

user) has successfully ful�lled hers. The system is ready to continue the dialogue

until the user's 'object of desire' is achieved.

5 Conclusion

This paper has described a cooperative planning system that plans appropri-

ate system responses in information-seeking dialogues. The planner is based on

the view that dialogue resembles a negotiation rather than a straightforward

question-answer sequence. Shared principles of rationality and cooperation pro-

vide a basis for such reasoning and dynamic knowledge of the context is e�ec-

tively used to reason about relevant dialogue continuations both in interpreta-

tion and generation. The system prototype experimentally uses a knowledge base

update procedure which models abductive reasoning. Integrity constraints are

expressed by describing inconsistent states of the system, and elimination of all

inconsistencies is tried by forming hypotheses that would count as explanations

for the input.

The system's exibility emerges during the reasoning processes, as a result of

the global system design. General pragmatic rules form declarative descriptions

of global pragmatic behaviour and constraints on the PLUS system. They can be

seen as a high-level operationalisation of some aspects of Allwood's rationality

principle and Grice's principles of cooperation. The PLUS system's communica-

tive adequacy is thus `hidden' in the inferences that the system draws in order

to maintain consistency of the context, and thus cooperativeness and helpfulness

are properties exhibited by the system as a whole, not independent principles

that govern the system's behaviour.

References

Allwood, J. (1976). Linguistic Communication as Action and Cooperation. GML2,

Department of Linguistics, University of G�oteborg.

Allwood, J. and H.C. Bunt (1992). Pragmatics in PLUS. Theoretical Underpinnings.

PLUS Project Report.

Appelt, D. E. and M. E. Pollack (1991). Weighted Abduction for Plan Ascription.

User Modeling and User-Adapted Interaction 1:4.

Black, W.J. and H. Cunningham (1992). The PLUS Surface Generator Design. PLUS

Project Report.

Bunt, H. C. (1990). DIT - dynamic interpretation in text and dialogue. In L. K�alm�an

and L. P�olos Papers from the Second Symposium on Language and Logic. Akademiai

Kiad�o, Budapest.

Bunt, H. C. (1991). Dynamic interpretation and dialogue theory. In The Second

Venaco Workshop on Multimodal Dialogue. Acquafredda di Maratea.

Cavalli, V., A. Guessoum, T. Lager, R. Meyer, J. Nivre (ed.) and N. Underwood

(1992). Pragmatic Knowledge in PLUS. Part I - World and Application Model.

PLUS deliverable.

Bego, H. (ed.), B. Beskow, H. Bunt, A. Derain, L. Horel, K. Jokinen, W. Kraaij,

D. Pernel and G. Tabuteau (1992). Pragmatic Knowledge in PLUS. Part III -

Pragmatic Rules. PLUS deliverable.

Gallagher, J. and A. Guessoum (1992). Abduction and Integrity Recovery for the

Control of Knowledge Base Interaction. Pre-prints of the ESPRIT/PLUSWorkshop

on Computational Pragmatics. Alghero, Sardinia, September 1992.

Gallagher, J., A. Guessoum (ed.), R. Haidan and R. Meyer (1992). Reasoning Tools

and Pragmatic Reasoning. A PLUS working paper.

Guessoum, A. and J. W. Lloyd (1990). Updating Knowledge Bases. New Generation

Computing. 8:1, 71-89.

Guessoum, A. and J. W. Lloyd (1991). Updating Knowledge Bases II. New Generation

Computing. 10:1, 73-100.

Hobbs, J. R., M. Stickel, D. Appelt and P. Martin (1990). Interpretation as Abduc-

tion. Technical Note No. 499. Arti�cial Intelligence Center, SRI International,

Menlo Park.

Jokinen, K., W. Kraaij, R. Meyer, J. Nivre, D. Pernel, H. Bunt and C. Godin (ed.)

(1992). Pragmatic Knowledge in PLUS. Part II - Discourse Model. PLUS deliver-

able.

Lascarides, A. and J. Oberlander (1992). Abducing Temporal Discourse. In: Aspects

of Automated Natural Language Generation. Dale, R., Hovy, E., R�osner, D., and

Stock, O. (Eds.). Springer-Verlag, Berlin, pp. 167-182.

McCoy, K. and J. Cheng (1990). Focus of attention: Constraining what can be said

next. In: Natural Language Generation in Arti�cial Intelligence and Computational

Linguistics. Paris, C. L., Swartout, W. R., and Moore, W. C. (Eds.). Kluwer Aca-

demic Publishers, Norwell, MA, pp. 103-124.

Moeshler, J. (1989). Mod�elisation du dialogue. Hermes, Paris.

Moore, J. D. and W. R. Swartout (1990). A Reactive Approach to Explanation: Tak-

ing the User's Feedback into Account. In: Natural Language Generation in Arti�-

cial Intelligence and Computational Linguistics. Paris, C. L., Swartout, W. R., and

Moore, W. C. (Eds.). Kluwer Academic Publishers, Norwell, MA, pp. 3-48.

Norwig, P. and R. Wilensky (1990). A Critical Evaluation of Commensurable Ab-

duction Models for Semantic Interpretation. In: Proceedings of COLING90. Karl-

gren, H. (Ed.). pp. 225-230.

Pollack, M. E., J. Hirshberg and B. L. Webber (1982). User participation in the rea-

soning process of expert systems. Technical report CIS-82-10, University of Penn-

sylvania, 1982.

Reiter, E. (1990). Generating Descriptions that Exploit a User's Domain Knowl-

edge. In: Current Issues in Natural Language Generation. Dale, R., Mellish, C.,

and Zock, M. (Eds.). Academic Press, pp. 257-285.

Roulet, E. (1986). Compl�etude interactive et mouvements discursifs. Cahiers de

Linguistique Fran�caise. 7, 189-206.

This article was processed using the L

a

T

E

X macro package with LLNCS style