CONVERSATIONAL AGENT IN ARGUMENTATION
Updating of Information States
Mare Koit
Institute of Computer Science, University of Tartu, J. Liivi 2, Tartu, Estonia
Keywords: Dialogue model, Reasoning model, Conversational agent, Information state, Update rules.
Abstract: The paper describes a computational model that we are implementing in an experimental dialogue system.
Conversation process is modelled where one participant is trying to influence his/her partner to agree to do
an action. In the paper we concentrate on the representation of information states of the conversational agent
and update rules which allow moving from one information state into another. Information state includes a
partner model which consists of evaluations of different aspects of the action under consideration. The
partner model is changing, based on the arguments and counter-arguments presented during the interaction.
As a practical realization of the model we have in view a computer program which we call communication
trainer.
1 INTRODUCTION
Modelling of conversational agents and development
of dialogue systems is aimed to make interaction of
human users with the computer more convenient.
Conversational agents communicate with users in
natural language in order to make travel
arrangements, answer questions about weather or
sports, route telephone calls, act as a general
telephone assistant, or perform even more
sophisticated tasks (Jurafsky and Martin, 2008).
Four kinds of dialogue management
architectures are most common. The earliest and
also one of the most sophisticated models of
conversational agent behavior is based on the use of
planning techniques (Allen, 1994). Plan-based
dialogue models take into account communicative
goals of dialogue participants and ways of their
achieving, and offer flexibility of interaction with
the computer but their creation and implementation
on the computer is hard.
The two simplest and most commercially
developed architectures are finite-state and frame-
based (Wilks et al., 2005). The existing dialogue
systems that interact with a user in natural language
are mostly implemented as simple finite state
automata which use regular expressions. In this way,
it is possible to achieve robustness as needed in
practical implementations because user’s options
and vocabulary are limited in every dialogue state.
Still, these systems lack the flexibility and
functionality which are important characteristics of
human-human communication.
The most powerful are information-state
dialogue managers (Traum and Larsson, 2003).
Information state represents cumulative additions
from previous actions in the dialogue, motivating
future actions. The functions of the dialogue
manager can be formalised in terms of information
state update. The information state may include
aspects of dialogue state and also beliefs, desires,
intentions, etc. of dialogue participants.
We are dealing with interactions where the goal
of one of the participants (A) is to get the partner (B)
to carry out a certain action D (cf. Koit and Õim,
2004, Koit et al., 2009). A as initiator of the
communication makes a proposal to the partner B to
do an action D. If B refuses then A must influence
him/her in the process of communication trying to
see on which step of the reasoning the partner
reached the negative decision.
In this paper, we will develop the model
considered in (Koit et al., 2009). The paper has the
following structure. In section 2 we give an
overview of modelling the communication process
between two participants. A model of conversational
agent which involves a reasoning model will be
presented. Section 3 considers interaction with the
conversational agent as updating of information
states. Section 4 discusses some aspects of
implementation of the model and section 5 makes
conclusions.
375
Koit M..
CONVERSATIONAL AGENT IN ARGUMENTATION - Updating of Information States.
DOI: 10.5220/0003636503750378
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 375-378
ISBN: 978-989-8425-80-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 MODELLING THE
COMMUNICATION PROCESS
Let us consider communication between a
conversational agent A and its partner B (another
conversational agent or human user). The process is
defined if the following is given (Koit et al., 2009):
1) set G of communicative goals where both
participants choose their own initial goals (G
A
and
G
B
, respectively). In our case , G
A
= “B makes a
decision to do D“
2) set S of communicative strategies of the
participants. A communicative strategy is an
algorithm which a participant uses for achieving
his/her communicative goal. This algorithm
determines the activity of a participant at each
communicative step
3) set T of communicative tactics, i.e. methods of
influencing the partner. For example, A can entice,
persuade, or threaten B in order to achieve its goal
G
A
4) set R of reasoning models which is used by
participants when reasoning about an action D. A
reasoning model is an algorithm the result of which
is a positive or negative decision about the object of
reasoning (in our case, an action D)
5) set P of participant models, i.e. a participant’s
depiction of himself/herself and his/her partner:
P = {P
A
(A), P
A
(B), P
B
(A), P
B
(B)}
6) set of world knowledge
7) set of linguistic knowledge.
2.1 Reasoning Model
The reasoning process of a subject who should make
a decision, to perform an action D or not (in our
case, B), consists of a sequence of steps where the
resources, positive and negative aspects of D will be
weighed. Partner (A) cannot take part in this
reasoning process explicitly. (S)he can direct the
reasoning of B only by giving information about
certain aspects of D, by stressing the positive aspects
of D and downgrading the negative aspects. Positive
aspects are pleasantness and usefulness of doing D
for B but also punishment for not doing D if D is
obligatory. Negative aspects are unpleasantness and
harmfulness of doing D and punishment for doing D
if D is prohibited.
The reasoning model consists of two parts: 1) a
model of human motivational sphere; 2) reasoning
schemes. We represent the model of motivational
sphere of a subject by the following vector of
weights assigned by him/her to different aspects of
an action:
w = (w(resources), w(pleasant), w(unpleasant),
w(useful), w(harmful), w(obligatory), w(prohibited),
w(punishment-for-doing-a-prohibited-action),
w(punishment-for-not-doing-an-obligatory-action)).
In the description, w(pleasant), etc. means
weight of pleasant, etc. aspects of D. Such a vector
(w
AB
) is used by A as the partner model P
A
(B). The
weights of the aspects of D are A’s beliefs about B.
When interacting, A is making changes in the partner
model if needed.
The second part of the reasoning model consists
of reasoning schemes that supposedly regulate
human action-oriented reasoning. A reasoning
scheme represents steps that the agent goes through
in its reasoning process; these consist in computing
and comparing the weights of different aspects of D;
and the result is the decision to do or not to do D (cf.
Koit and Õim, 2004). In the motivational sphere
three basic factors that regulate reasoning of a
subject concerning D are differentiated. First,
subject may wish to do D, if pleasant aspects of D
for him/her overweigh unpleasant ones; second,
subject may find reasonable to do D, if D is needed
to reach some higher goal, and useful aspects of D
overweigh harmful ones; and third, subject can be in
a situation where (s)he must (is obliged) to do D – if
not doing D will lead to some kind of punishment.
We call these factors wish-, needed- and must-
factors, respectively. They trigger the reasoning
procedures wish, needed and must, respectively.
It is supposed here that the dimensions
pleasant/unpleasant, useful/harmful, etc. have
numerical values and that in the process of reasoning
(weighing the pro- and counter-arguments) these
values can be summed up.
In general this reasoning model follows the ideas
of the Belief-Desire-Intention model (Allen, 1994).
2.2 Reasoning in Interaction
In the goal base of one participant (the
conversational agent A) a goal G
A
gets activated. A
checks the partner model – supposed weights of the
aspects of D. Then A chooses tactics of influencing
of B (e.g. to persuade B, i.e. to stress the usefulness
of D). Therefore, the agent sets up a sub-goal – to
trigger in B a certain reasoning process (in case of
persuading, by the needed-factor). A plans the
dialogue acts and determines their verbal form as the
first turn tr
1
. This turn triggers a reasoning process
in B where two types of procedures should be
distinguished: the interpretation of A’s turn tr
1
and
the generation of B’s response tr
2
. The turn tr
2
triggers in A the reasoning cycle, A builds a new turn
tr
3
. Dialogue comes to an end, when A has reached
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
376

or abandoned its goal.
3 INTERACTION AS UPDATING
OF INFORMATION STATES
3.1 Representation of Information
States
The key of an information state is the partner model
which is changing during the interaction.
There are two parts of an information state of a
conversational agent – private (information
accessible only for the agent) and shared (accessible
for both participants). The private part consists of
the following information slots:
 Current partner model (vector w
AB
of weights –
A’s picture about B)
• A tactic t
i
A
which A has chosen for influencing B
 Reasoning procedure r
j
which A is trying to
trigger in B and bring to a positive decision (is
determined by the chosen tactic, e.g. when
persuading, A triggers the reasoning procedure
needed in B)
 Stack of (sub-)goals under consideration. In the
beginning, A puts its initial goal into the stack
(“B decides to do D”). In every information state,
the stack contains an aspect of D under
consideration (e.g. when A is persuading B then
usefulness is on the top)
 Set of dialogue acts DA={d
1
A
, d
2
A
, …, d
n
A
}.
There are the following DA-s for A: proposal,
assessments for increasing or decreasing weights
of different aspects of D for B, etc.
 (Finite) set of utterances as verbal forms of DA-
s, incl. utterances for increasing or decreasing the
weights (“arguments for/against”) U={u
i1
A
, u
i2
A
,
…, u
iki
A
}. Every utterance has its own
weight/numerical value: V={v
i1
A
, v
i2
A
, …, v
iki
A
}
where v
i1
A
, etc. is the value of u
i1
A
, etc.,
respectively. Every argument can be chosen by A
only once.
The shared part of an information state contains
 Set of reasoning models R={r
1
,…,r
k
}
 Set of tactics T={t
1
, t
2
, …, t
p
}
 Dialogue history – the utterances together with
participants’ signs and dialogue acts p
1
:u
1
[d
1
],
p
2
:u
2
[d
2
],…, P
i
:u
i
[d
i
] where p
1
=A, p
2
, etc. is A
or B.
3.2 Update Rules
There are different categories of update rules which
will be used for moving from the current
information state into the next one:
I. Rules used by A in order to generate its turns:
1) For the case if the “title” aspect of the used
tactic is located on top of the goal stack (e.g. if
the tactic is persuasion then the “title” aspect is
usefulness)
2) For the case if another aspect is located on the
“title” aspect of the used tactic (e.g. if A is
trying to increase the usefulness of D for B but
B argues for unpleasantness, then the
unpleasantness lies over the usefulness)
3) For the case if there are no more utterances for
continuing the current tactic (and a new tactic
should be chosen if possible)
4) For the case if A has to abandon its goal
5) For the case if B has made the positive decision
and therefore, A has reached the goal.
II. Rules used by A in order to interpret B’s turns.
Special rules of the category I exist for updating
the initial information state.
4 DISCUSSION
When A tries to bring B to a decision, A uses several
statements to increase the weights of the positive
aspects and to decrease the weights of the negative
aspects of the action D under consideration. If B
indicates a certain aspect which actual weight (too
low or too high) does not allow him/her to do D then
A simply can choose a statement for attacking this
aspect. If B does not indicate a certain reason of
rejection then A only can stress the usefulness when
persuading.
Let us consider a brief example where the action
D is “to prepare a potato salad” (cf. Koit et al.,
2009). A has such a partner model that the reasoning
procedure needed would give a positive decision. A
will implement the tactic of persuasion.
The initial information state of A is as follows.
Private part
 Initial partner model
w
AB
= (w
AB
(resources)=1, w
AB
(pleasant)=3,
w
AB
(unpleasant)=0, w
AB
(useful)=7,
w
AB
(harmful)=0, w
AB
(obligatory)=0,
w
AB
(prohibited)=0, w
AB
(punishment-for-doing-
a-prohibited-action)=0, w
AB
(punishment-for-
not-doing-an-obligatory-action)=0)
 The tactic chosen by A – persuasion
 A tries to trigger the reasoning procedure
needed in B
 Stack of goals under consideration contains
only A’s initial goal
 Set of dialogue acts at A’s disposal
CONVERSATIONAL AGENT IN ARGUMENTATION - Updating of Information States
377

 Set of utterances for expressing the dialogue
acts, together with their values {I will help you
– value 1, etc.}.
The shared part of the initial information state
contains
 The reasoning procedures wish, needed, and
must
 The tactics of enticement, persuasion, and
threatening
 Dialogue history – empty set.
A (computer): Please prepare a potato salad.
[Proposal]
B (user): I do not have enough time. [Refusal to
do D + assertion for decreasing the weight of
resources]
Therefore, the actual value of w
B
(resources) is 0.
The computer tries to increase the value:
A: I will help you. [Rejection of the argument +
assertion for increasing the weight of resources]
B: It is very hot in the kitchen. [Refusal to do D
+ rejection of the argument + assertion for
increasing the weight of harmfulness]
Therefore, the weight w
AB
(harmful) has to be
corrected in the user model:
A: My kitchen has good ventilation. [Rejection
of the argument + assertion for decreasing the
harmfulness],
etc.
An experimental dialogue system is implemented
which in interaction with a user can play the role of
A. At the moment, the computer operates with
semantic representations of linguistic input/output
only, the surface linguistic part of interaction is
provided in the form of a list of ready-made and
classified utterances both for the computer and user.
5 CONCLUSIONS
We are dealing with interactions where the goal of
one participant is to get the partner to carry out a
certain action. The paper describes a computational
model that we are implementing in an experimental
dialogue system. We concentrate on the
representation of information states and update rules.
Information state includes a partner model which
consists of evaluations of different aspects of the
action under consideration. The partner model is
changing during the interaction, based on the
arguments and counter-arguments presented. As a
practical realization of the model we have in view a
computer program which we call communication
trainer.
We are continuing our work in the refining the
model, considering different scenarios, e.g. A and B
have opposite goals and one of them has to abandon
his/her initial goal (as considered so far), or they
collaborate in order to achieve a common goal; both
of A and B are conversational agents with their own
information states and update rules. Different
communicative strategies/tactics used by
participants will be evaluated taking into account
their success in achieving the initial goal.
ACKNOWLEDGEMENTS
This work is supported by the European Regional
Development Fund through the Estonian Centre of
Excellence in Computer Science (EXCS), and the
Estonian Science Foundation, grants 7503 and 8558.
REFERENCES
Allen, J., 1994. Natural Language Understanding. Second
Edition. The Benjamins/Cummings Publ. Co.
Redwood City etc., 678 p.
Jurafsky, D., Martin, J., 2008. Speech and Language
Processing: An Introduction to Natural Language
Processing, Computational Linguistics, and Speech
Recognition. Prentice Hall, 1028 p.
Koit, M., Õim, H., 2004. Argumentation in the Agreement
Negotiation Process: A Model that Involves Natural
Reasoning. In Proc. of the Workshop W12 on
Computational Models of Natural Argument. 16th
European Conference on Artificial Intelligence.
Valencia, Spain, 53–56.
Koit, M., Roosmaa, T., Õim, H., 2009. Knowledge
representation for human-machine interaction. In
Proceedings of the International Conference on
Knowledge Engineering and Ontology Development:
International Conference on Knowledge Engineering
and Ontology Development, Madeira (Portugal), 6-8
October 2009. Jan L.G. Dietz (Ed.), Portugal:
INSTICC, 396–399.
Traum, D., Larsson, S., 2003. The Information State
Approach to Dialogue Management. In Current and
New Directions in Discourse and Dialogue, J. van
Kuppevelt and R. Smith (Eds.), Kluwer, 325–353.
Wilks, Y., Webb, N., Setzer, A., Hepp, M., Catizone, R.,
2005. Machine learning approaches to human dialogue
modelling. In Text, Speech and Language Technology.
Advances in Natural Multimodal Dialogue Systems,
Vol. 30. Springer, 355–370.
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378