Pre-print of full paper presented at CONVERSATIONS 2017 - an international workshop on
chatbot research and design, November 22, in conjunction with the 4th International
Conference on Internet Science, Thessaloniki, Greece. The final version of the paper will be
published in the conference post-proceedings as part of Springer LNCS.

Creating Dialogues using Argumentation andSocial Practices
Frank Dignum and Floris Bex
Department of Information and Computing Sciences,
Utrecht University, The Netherlands{F.P.M.Dignum,F.J.Bex}@uu.nl

Abstract. Research in chatbots is already more than fifty years old,
starting with the famous Eliza example. Although current chatbots might
perform better, overall, than Eliza the basic principles used have not
evolved that much. Recent advances are made through the use of massive learning on huge amounts of resources available through Internet
dialogues. However, in most domains these huge corpora are not available. Another limitation is that most research is done on chatbots thatare used for focused task driven dialogues. This context gives a natural focus for the dialogue and facilitates the use of simple reactive rules
or frame-based approaches. In this paper, we argue that if chatbots areused in more general domains we have to make use of different types ofknowledge to successfully guide the chatbot through the dialogue. We
propose the use of argumentation theory and social practices as two general applicable sources of knowledge to guide conversations.

1

INTRODUCTION

With the current surge in AI the interest in using chatbots, for example, for client
contacts, has also increased. Due to the amazing results of deep learning and theexcellent framing of the capabilities of their chatbots by companies like Appleand Google, people expect chatbots to ”learn” to give the ”correct” reaction to
any user input. However, whereas techniques from deep learning can be used insome particular domains or applications, they are of no use in other domains.
For example, learning the right behaviour becomes difficult when only small
amounts of data is available. Furthermore, some features of interactions are ofa social nature and can only be inferred in an indirect way using knowledge ofthe context, which is not represented in the textual data.
Chatbots have mainly been successful in situations where the user interacts
with the chatbot for a very specific purpose that is determined beforehand. For
example, in a recent system [4] we use a chatbot to interact with a user that isreporting fraud about an e-commerce transaction. Even though this application
is far from simple it is clear from the start which information is needed from theuser and what the goal of the interaction is. The user also sees the form and, eventhough the interaction is in natural language, the context is very focused andclear. For example, when the system asks what happened, it is clear that only

events pertaining to the particular e-commerce transaction should be related.
Thus the parsing of the input can concentrate on keywords and particular phrases
indicating order of events.
More interesting (and difficult) situations arise when the purpose of an interaction is to find out what exactly the needs of the user are. This can be e.g.
a user finding out whether it is interesting for him to change his mortgage toanother format, or whether a user would need a license to extend the roof of his
house with a standard dormer structure or whether insisting on a crime reportmight lead to a possible restitution of the damages. In all these cases the purposeof the interaction is no longer to determine parameters for a specific action of theorganization, but rather it is the joint determining which type of action would
be appropriate. The main difference seems to be that the dialogue is becominga real two-way dialogue where both parties seek and give information and areplanning steps in the dialogue which are not fixed beforehand.
We argue that for the above situations we should take an agent-based approach. Current chatbot technology is directed to create a response that makes
sense in a particular context. Thus there are means to keep track of the dialogue state and adapt responses based on history of the dialogue. However, thetechnology is not geared towards creating the dialogue structure in collaboration
with the user. Agent technology gives a different perspective on the dialogue,
where the agent is not only reacting to input of the user, but also can pursue its
own goals or joint goals with the user. This leads to dialogues as joint intentionsin the sense of [7]. Taking this perspective also facilitates two approaches that
support dialogue management. First of all it facilitates the use of argumentationin dialogues [13]. The agent can reason about the input of the client as being anargument in the context of the dialogue history. This position indicates the typeof reaction the agent can give and for what purpose the reaction is intended. Argumentation can also be used to structurally guide the dialogue to a conclusion
accepted by both parties.
Secondly, we can use social practices as concepts that shape our social interactions. Social practices describe many aspects of the context of the interaction
and thus give rise to expectations of what will happen at what stage of the interaction, who has initiative, etc. For example, when a client of a bank contacts
the bank he can expect that the bank wants to know his bank account number.
This will facilitate further interaction because through this number the bank can
retrieve all information about the client. Also in many formal interactions theorganization will summarize and check all agreements that have been made withthe client at the end of the meeting. These types of dialogue patterns not only
give a context to parts of a dialogue but help shape the interaction and focus theattention and direct actions towards a specific joint goal to be achieved. These
aspects can be used to modularize the rules of interaction and focus attention
to topics fitting in each phase of a dialogue. This will limit the freedom of thenatural language used in a very natural way and also in many case convert natural language parsing into pattern matching with the pattern being the expected
input at a certain time in the dialogue.

In the rest of this paper we will give some background on social practices andargumentation in section 2. Subsequently we will show in section 3 how thesecan be specifically useful for chatbots. In section 4 we give some conclusions andpointers to future work.

2

Background

2.1

Social Practices

Social practices can be seen as patterns which can be filled in by a multitudeof single and often unique actions [17], that endure between and across specificmoments of enactment. Through (joint) performance, the patterns provided by
the practice are filled out and reproduced. Each time it is used, elements ofthe practice, including know-how, meanings and purposes, are reconfigured andadapted [18]. Therefore the use of social practices includes a constant learningof the individuals using the social practice in ever changing contexts. In this way
social practices guide the learning process of agents in a natural way.
In [18] the social aspect of social practices is emphasized by giving the social
practice center stage in interactions and letting individuals be supporters of thesocial practice. It shows that social practices are shared (social) concepts. The
mere fact that they are shared and jointly created and maintained means that
individuals playing a role in a social practice will expect certain behaviour andreactions of the other participants in the social practice. Thus it is this aspectthat makes the social practices so suitable for use in designing interactive systems
and especially where the interaction is not completely structured beforehandsuch as in dialogues between the system and the user.
Characteristics of Social Practices Researchers in social science have identified three broad categories of elements of practices [10]:– Material: covers all physical aspects of the performance of a practice, including the human body (relates to physical aspects of a situation).
– Meaning: refers to the issues which are considered to be relevant with respect
to that material, i.e. understandings, beliefs and emotions (relates to social
aspects of a situation)– Competence: refers to skills and knowledge which are required to performthe practice (relates to the notion of deliberation about a situation).
These components are combined by individuals when carrying out a practice.
Each individual embeds and evolves (through conditioning) meaning and competence, and adopts material according to its motives, identities, capabilities,
emotions, and so forth, such that it implements a practice. Individuals and societies typically evolve a collection of practices over time that can be appliedin different situations. Although social practices provide a handle for modellingthe deliberation of socially intelligent agents because they seem to combine theelements that we require for socially intelligent behaviour, they are a relatively

novel and vaguely defined concept from sociology that cannot be just applied inagent systems. Thus we have tried to convert the above very general and vague
definition into a slightly more concrete and usable format. Table 1 informallydefines the aspects that are relevant in a typical social practice.
The material aspects of social practices are divided over the (physical) context, activities and plan patterns. The physical context contains elements fromthe environment where we distinguish between objects (resources) and actors
and the locations of all elements. Location can again be physical location but
also temporal location (thus the time of day or year). The activities indicate
the normal activities that are expected within the practice. In a doctor consult these are greeting, explaining symptoms, discussing treatment, recapture
findings, questioning information, etc. Note that not all activities need to be
performed. They are meant as potential courses of action. In the same way theplan patterns describe usual patterns of actions that can be used as frames fromwhich a planning process can start.
The meaning of a social practice is divided over the social interpretation of
the context and the social meaning of the activities performed in the practice.
The first part actually determines the social context in which the practice is used.
Of course, this part can be highly subjective with different agents interpreting
signals in their own way. The social context also contains the roles and norms
that are attached to the situation. E.g. a person can be a doctor or patient,which leads to different expectations of this person. Norms also give rules of(expected) behaviour within the practice. The part that we have now coined themeaning of the practice refers to the social meaning of the activities that are (orcan be) performed in the practice. Thus they indicate social effects of actions.
The competencies are still present in the form of capabilities the agent shouldhave to perform the activities within this practice.
The table describing the social practices as given above starts resembling thelist of aspects that also play a role in agent organization models (see e.g. [8]).
It can be seen as an analogue of the connection between imposed and emerging
norms. Both organizations and social practices give a kind of structure to theinteractions between agents. However, organizations provide an imposed (topdown) structure, while the social practices form a structure that arises fromthe bottom up. Thus where organizational interaction patterns indicate minimal patterns that agents should comply with, the patterns in a social practiceindicate minimal patterns that can and are usually used by the agents.
A final, very important, observation is that social practices are not the sameas scripts. They do not generate a complete, deterministic protocol, but rather
give a set of expectations that can be used to guide the interaction, but also
can be deviated from. Secondly, social practices do not just indicate expectedactions, but also give information about expected resources,information and timing. Moreover, they give social meaning to actions in the context of the social
practice that facilitate social reasoning and planning during the interaction.

Meaning
Context
Actors The actors in the social practice.
Roles The roles such as client, consultant, account manager
Resources The set of resources available for the social practice. This can be icons
on a joint window, information or physical objects
Positions Position in space of resources and actors.
Activities
Basic actions A finite number n of basic actions in the social practice. Joint plans arebuilt using these actions.
Capabilities A function that indicates the set of actions an actor is capable of performing.
General preconditions Conditions assumed to hold at the start of the practice.
Meanings
Purpose The purpose of an action or interaction is the reason it is performed
and usually indicates the intended result. E.g. a query is posed withthe purpose to get relevant information.
Promote Indicates that in the context of this social practice, an action α promotes value v. E.g. waiting for your turn promotes cooperation. For a
more formal characterization of the promotes relation see [20].
Counts-as Indicates that within this social practice executing action α is seen as
performing β. E.g. summarizing the dialogue counts-as moving to theend of the dialogue
Expectations
Plan pattern The plan pattern indicates the landmarks (or goal state) of each partof the interaction, the actor/role having initiative for each part and theorder in which the parts are executed.
Norms There are Obligations and Prohibitions. E.g. the chatbot cannot end
the dialogue.
Triggers Indicates that if a condition holds then all actors believe some specificaction will be performed next. E.g. a question to explain something isfollowed by an explanation
Start Condition Set of conditions that actors all believe to be true when the social
practice starts.
Duration Expected durations of actions and plans. Note, not all actions need tohave an expected duration.
Table 1. Description of all aspects of the social practice2.2

Argumentation

Where social practices create a context for the dialogue in which actions can beinterpreted and expected, the argumentation supports the building of the actual
dialogue parts. The social practice delivers so-called plan patterns that divide
the whole dialogue into a number of phases each with its own goal state. These
goal states are the conditions that have to be argued for using the argumentation
theory.
In many open dialogue situations an argumentation dialogue with an associated argument graph is a better basis for dialogue management than decision
trees coupled with traditional databases. An argument graph is a formal representation of statements that support and reject claims [6, 9]. Such a formallydefined graph makes it possible to store and structure argumentation dialogues
and the arguments put forward in these discussions [5], as well as the reasoning
over these arguments [16, 3]. Argumentation dialogues are a method to exchange
information efficiently by only requesting information that is necessary to support some main claim of the dialogue given the history of the dialogue. For
instance, if the main claim is whether or not an incident is a case of fraud, thenargumentation dialogue management helps us to determine what information isrelevant with respect to supporting or rejecting that claim. If from the reasoning
over the arguments in the graph it is clear that the status of the main claim
cannot change (supported or rejected) then we may terminate the dialogue. In
other words, argumentation dialogues support both reasoning with structured
arguments and dialogue coherence [15]. Reasoning with arguments is very useful for further processing the results of the dialogue (cf. [4]), and is not easily
achievable with decision tree based dialogue management. Argumentation dialogue systems are also easier to maintain: we can easily add or remove arguments
without the need to update a complex decision tree on when to ask which information. Finally, a statement with multiple facts is relatively easy to processsince we can add all facts to the graph and reason about the arguments thatthey support or attack.

3

Social Practices and Argumentation for Dialogue
Management

In this section we will show how the intuitions of social practices and argumentation can be concretely used to implement a dialogue management system. The
system is based on the SALVE system [1] used to train medical students to have
conversations with patients.The following sections describe the modules using as
example a doctor consult to diagnose a patient. The purpose of this practice isthe arrangement of an health care plan for the patient. The plan assumes the accomplishment of dialogue activities such as greetings, health problem description
and therapy acquisition.

Interaction Module
Dialogue Module

GUI

Scenes categories

S-AIMLSP categories
Commons categories

Score Module

Representation and Interpretation
Scenes model & rules

Emotion Module

Emotion
Empathic

SP model & rules

Social behaviour

General SP model & rules
Empathic Module

Upper Ontology model

Communication skills

Fig. 1. The agent architecture

3.1

The agent architecture

We assume that the agent has already identified the current social practice [1].
This is accomplished by means of the Interaction and Representation and Interpretation (R&I) modules (see figure 1). The architecture also includes a scoremodule used to assess the utility of the dialogue. At the end of the consultation
the doctor should have a clear diagnosis of the illness of the patient and a treatment plan. The patient should understand what is wrong with him and have all
information needed to follow the treatment plan. If the goal is arguably achieved
the dialogue gets a full score, otherwise the utility decreases. The argumentation
system can be used to check the score against the argumentation tree that isbuilt during the dialogue and arguments can be given of why a full score is notreached (yet).
Interaction Module The interaction module is composed of a graphical interface where an avatar is used to express the emotions and reactions of the system,
and a dialogue sub module that allows the agent to manage the conversation,interpret the utterances of the players and recognize events. The dialogue sub
module is implemented by means of the S-AIML technology [2], an extensionof the AIML language [12], that allows to bind the categories to specific practices and their activities. The S-AIML language and its relative processor, areenhancements of the traditional dialogue engine of Alice that allows the chatbot
to manage the dialogue according to a specific social practice. The language isused to describe possible question - answer modules, called categories, that canbe matched during the conversation. The categories can be matched in differenta)<scene name="greetings">b)<category><category><pattern><set>greetings</set></pattern><template>
Hello!
<insert packageName="sp.anamnesi.greetings"
typeName="GreetingsReceived" /></template></category><pattern>why are you here</pattern><template><insert packageName="sp.anamnesi.health_problem"
typeName="HealthProblemAsked" />I'm experiencing one of my usual<getDroolsTemplate />. It's quite strong.
</template></category>Fig. 2. S-Aiml examples

practices (Common Categories), or can be organized according to the current
social practice (SP Categories) by means of a social practice tag or more specifically according to a scene of the practice (Scenes Categories) by means of the(scene tag). Moreover it is possible to indicate specific preconditions that haveto be matched using the (precondition tag).The category shown in figure 2-aallows for an interpretation of a greeting as the accomplishment of the subgoal
associated to the greeting scene, and as result triggers a GreetingsReceived event.
The category shown in figure 2-b allows for a transition to the next scene ofthe practice. In particular, if the user asks the agent the reason of the appointment, there is a transition to the health problem scene and the querying of the
R&I module to get the specific health problem of the scenario. The answer of theagent will be: ”I’m experiencing one of my usual headaches. It’s quite strong”.
Representation and Interpretation This module organizes the knowledge
and the rules required by the agent to correctly interpret the events recognizedby the interaction module. Similarly to the previous module, there is generalknowledge and knowledge specific for the social practices. The general knowledge
is related to the agent beliefs and it also includes the formalization of the social
practice module, its main components, the rules for starting and closing thepractices and to choose and perform a plan pattern. Taking the doctor consult
as example we briefly sketch some of this knowledge according to the social
practice.
From the context information of the social practice we get the knowledge of
who the actors are and what their r oles are. In the example the avatar will be apatient and the student plays the doctor role. For a specific session the patient
will be a specific patient with name, history, etc. and the user will be asked forher profile (name, age, etc.). We also get the resources and positions form thesocial practice context. In the example the patient’s health file is a resource, but
it can also be a form that is filled in jointly during the dialogue. Positions are
not really important in this scenario, but can be helpful when more parties are
involved in the dialogue.
The activities of the social practice indicate the activities that the participants can execute. In dialogues these are of course dialogue acts, but also can beactions like pointing or filling a form and also can indicate mental activities likemaking a diagnosis by the doctor. With the activities we also store their effects(both social as well as physical). The capabilities specify for each role whichcapabilities it has such that it can perform certain actions. Thus a doctor canmake a diagnosis, while the patient is not expected to do this. In the other handthe patient can tell his symptoms, while the doctor has no direct access to them.
This modularizes the type of acts that are expected from each participant in the
dialogue. Finally, the general preconditions indicate some common knowledge
that can be used and is assumed to be used by all parties in the interaction. E.g.
the patient comes to get a consult with the doctor and has some health problem.
The meanings of the social practice are used for giving direction and interpreting what happens in the dialogue. The whole social practice has a purpose.
In this case that the patient receives the proper health care plan. This goal ischecked whenever one of the parties wants to finish the dialogue in order to see
if the goal is reached. If not, the dialogue is resumed again. Also the different
phases in the practice have their own purpose. E.g. the information gathering
phase has as purpose for the doctor to have enough information to make a diagnosis. Thus the dialogue is focused on reaching this goal and it is explicitly
checked at each point of the phase. It leads to selection of dialogue acts that
contribute to this goal. The counts-as information in the social practice indicates the way actions are interpreted in the context of the practice. Thus if thedoctor says ”let me summarize”, she does not just indicates what she is goingto do, but also indicates that she thinks there will be no further discussion orquestioning and the consult is going to finish. Finally, the promotes information
indicates that certain ways of acting promote certain values. This will be used tomake choices between reactions in order to promote a certain value or generateemotions.
The expectations of the social practice are more actively used in the dialogue management. The plan patterns are used to provide the contexts of theinteractions. Thus they limit what is expected in each phase of the dialogue. The
triggers indicate some standard behavior that does not require any processing.
E.g. saying ”hello” at the beginning of the dialogue triggers a greeting. These
reactive rules make the dialogue processing more efficient as it can be handled
by the chatbot rules directly without having to go through a more extensive
reasoning phase. The norms are used to select standard actions in situations
where they are expected. Thus a doctor is expected to start the consult with an
inquiry about the health of the patient. Even though she might already see whatis wrong if symptoms are clearly visible. Finally start condition and d uration
are two types of information that are used to select options at the start and endof a dialogue (phase). Generally one knows that a doctor consult will take nomore than 20 minutes. Thus the expectation is that a doctor will direct the dialogue to the conclusion when this time limit is reached. Timing can thus be used
to direct action selection and also influences the general state of the dialogue
partners. E.g. if an answer takes a long time to come, inferences are made by
the doctor or the patient about the reason for this hesitation.
In conclusion we can say that a specific social practice carries implicit knowledge, therefore, depending on the selected social practice, the agent updates its
beliefs about the social interpretation of the elements with which it interacts.

a)

b)

rule "GreetingsReceivedInTime"when$startScene:EnterScene(scene.name=="greetings")$g:GreetingsReceived(this after[0ms,20000ms] $startScene )then
OCCHappenedEvent he=new OOCHappenedEvent();don(he,DesirableEvent.class);don(he,ProspectedRelevantEvent.class);
insert(he);insert(new ChangeOfSceneFromGoal());
end

rule "DesirableProspectedEventHappened"
when$d:OCCHappenedEvent(this isA ProspectedRelevantEvent, thisisA DesirableEvent)$agent:Emotion(this isA Agent)
then$agent.setSatisfaction($agent.getSatisfaction()+1);
end

Fig. 3. Drools examples

The knowledge carried by a practice includes the agents beliefs and expectations
in a practice, the rules for the generation of plans and the analysis of possible
norm violations. The rules embedded in a practice are used as a kind of shortcuts in the deliberation of the agent. The events triggered by the conversational
moves of the player, are analyzed according to the dimensions of interest in the
dialogue. As a specific example, a greeting received in time is interpreted as expected relevant desirable event according to the OCC classification of emotions[14] (figure 3-a) and will elicit positive emotions (figure 3-b). In the figure one
can see that information from the chatbot is exported to Drools1 which thenproduces values for parameters that are used to generate output of the chatbot
again.
We can also use Drools rules to check for information in the conversation by
extracting facts from utterances using natural language processing and building
an argument graph from those facts in order to determine whether, for example,
enough information is provided about the health care plan. The system then asks
the student through a single turn interaction to provide extra information. Thisway we can see how students describe certain facts. For instance, if the patientasks “Why did you decide to prescribe spironolacton?”, we may gather natural
language descriptions of reasons to prescribe that medicine. For example theresponses may include “Because I think spironolacton is important to support
your heart” or “Because the spironolacton is needed to complement the beta
blocker”. From these replies we may retrain our fact extraction module in order
to extract information from explanations and extract also more details.
From the extracted facts we need to construct an argument graph that either
supports or rejects the claim whether the patient can understand the reasons fortaking all medicines. This requires a knowledge base to model argumentationin the target domain. The knowledge base about the target domain is to beacquisitioned from the experts in the domain. It contains argumentation patterns
such as “spironolacton is used to support the heart functioning effectively”. The
argument graph is constructed by applying the argumentation patterns on thefacts. The system can see what information from the student could make thedifference between the main claim being supported or rejected. This missing
information forms a basis for the questions that the system may ask to thestudent. The responses provide extensions to the argument graph. Hence, if we
1

Drools is a tool to describe expert system rules. See www.drools.org for documentation and software.

can translate a dialogue to an argument graph, and we specify a utility forthe argument graph, then we can use this as the utility of the dialogue. The
utility ought to be maximal if the main claim is supported or rejected and no
possible information can be given by the student to change the status. Otherwise
the utility should deteriorate depending on the information that could still be
provided.
On the implementation side we can use the AIF ontology [6] to store argument
graphs and the knowledge base. The graphs are evaluated (to determine what
claims are supported or not by the argument graph) using Aspic+ semantics [16,3] and make use of the TOAST [19] tool.
3.2

User status

In the previous sections we have explained how the conversations with the user
are managed using social practices. We also showed how argumentation graphs
can be used to determine whether the dialogue has been useful. However, wehave not yet discussed the state of the complete dialogue from both the patient
and the student perspective. In this section we will further discuss how thisapproach supports the ”scoring” of a conversation. I.e. the overall success of the
dialogue as perceived by its participants.
Our approach using social practices and mental states of the agents suggests
a scoring approach that is not tied to the actual conversation moves (as is donein some dialogue games like Communicate! [11] , but rather to the mental statesthat the agents maintain and the deviations or confirmation of the social practicethat is followed. As we have shown, the user’s conversational moves lead tochanges in e.g. the emotional state of the conversational agent depending on thespecific content of that move. Thus the target of the system is to communicate
in a way to optimize the score of the user’s mental state. In a naive approach one
might then say that a move that makes a user angry, worried or impatient is bad(leads to a decrease in score). However, it is not always possible to avoid negative
emotions in a user. If the bank finds out that the user most likely cannot get the
mortgage she wants the bank needs to tell this and this might create all kindsof negative emotions in the user. The dialogue management should be geared
to how to cope with these emotions and use the conversation to decrease these
emotions in some way in the user. If a user is both worried and angry it canbe best to first alleviate his worries and after that on his anger, but if they aretackled in the reverse order it might still work in the end. Thus scores should
not be given to single states, but rather to sequences of states. E.g. a user gets anegative score if the perceived anger of the user stays above a certain level morethan five or ten moves and even gets a very negative score if the user is still
angry at the end of the conversation. So, rather than scoring individual states
we can give rules on the sequences of states that appear and their influence on
the score. A second issue is that there are several aspects that play a role inthe conversation and that the system should try to control. We have mentioned
empathy with the user in our context. Because the (felt) empathy is modeled asa separate aspect of the state of the user it can get a separate score. Having tohandle combinations of aspects the system now has to try to maximize the scoreson all aspects, which often cannot be done at the same time (i.e. in one move).
By attaching the scores to rules over state sequences it is now easily possible toshow that some orders of conversational moves lead to better scores than otherones without limiting the conversation to some predetermined order(s). So, we
get more flexibility and more accurate scoring of the systems performance. The
social practice is used for the scoring as this practice gives a context in which
the interaction takes place and it therefore also indicates the expected (rangeof) mental states of the participants during the conversation. Thus we can use
these mental states as a kind of target values for the system to achieve. Thus
scores become relative to a social practice rather than some absolute values,
which makes the scoring more realistic. E.g. a user should never be angry ina regular meeting with his bank account manager, but he can be expected to
become angry in a bad news conversation. Thus the user becoming angry is notalways bad, but is bad in certain contexts.

4

Discussion and Conclusion

In this paper we have discussed some new approach to dialogue management
using chatbots in combination with social practices and argumentation theory.
Although chatbot technology has progressed a lot and it is possible (also throughlearning) to generate reasonable responses to a wide range of inputs it still isinadequate for many domains. We have taken an example from the medical
domain as this shows very well that responses in some domains should reallyfulfill many requirements. When a student is learning to manage a dialogue withpatients in an anamnese consult the patient (played by the computer system)
should be believable and it is very important that the student learns to takeemotions of a patient into account as well as whether a patient really understands
his situation, whether the patient understands the health care plan, etc. So, in
this situation the patient should not just give any response to input of the student(trying to act the doctor role), but responses that indicate to the student thatcertain information is still missing and why it is needed as well.
The use of social practice theory provides a way to contextualize the dialogue
and thus providing all kinds of background information that can be subsequently
used to interpret the input of the student and also to guide the conversation. By
modularizing the interaction it prevents the explosion of possible combinationsof dialogue moves between the parties. By using a kind of state parameters we
can also interpret dialogue moves of the student in a better way.
Argumentation is used to keep track of whether the goal of a certain dialogue
phase has been achieved. The argumentation tree that is constructed can be used
to prove that certain aspects have not been covered yet and it can also be used
to explain why those parts are important. Moreover the tree can be used after
the dialogue is finished to learn about what happened in the dialogue and tofollow it up with other actions.

In this paper we have only briefly sketched how both argumentation theory
and social practices together can be used to enrich chatbot technology. We havealready built working systems for different applications using them separately.
This paper is a first step in showing how they can be combined in a more
powerful and robust dialogue management system. The next steps are twofold.
First we will actually combine the different parts of the implementations into
a prototype that can be used for a testbed. Secondly, we will start developing
management tools to support the description of social practices and ontologies
needed for applications of the system in actual real-time situations. A longer
term development will be to introduce machine learning techniques to refineboth the ontologies for an application domain as well as the social practices.
This will lead to less development effort, but also to an adaptation to the useof the tool in specific domains and for specific types of users. Thus a dialogue
can be adapted when interacting with younger children or older people, or withpeople with different cultural backgrounds and expectations. All these groups
use different language and have different expectations on how they are addressed.
This can be accommodated by the system while being based on the same basic
interaction pattern.

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