The document proposes a system called SALVE that uses social agents to guide learning in virtual environments. SALVE aims to improve on traditional chatbot approaches by incorporating models of social practices to provide structure to conversations. The system architecture integrates a chatbot interface with a rule-based engine to track an agent's state within a social practice. Examples show how rules can generate positive or negative emotions based on whether greetings are received in a timely manner. The system is intended to give players more freedom in conversations while simplifying the representation of conversation knowledge. Future work includes finalizing a serious game implementation and validating the approach.

1. Social Agents for Learning in
Virtual Environments
Agnese Augello1, Manuel Gentile2 , Frank Dignum3
1
ICAR - National Research Council of Italy,
2
ITD - National Research Council of Italy
3
Utrecht University, The Netherlands

2. Outline
Learning social and communication skills
Social skills training and SGs
The proposed solution: SALVE
Architecture
Some examples

3. Why social skills?
interpersonal,
social and
communicative
competences
social,
psychological
and occupational
well-being are
ensured by
academic or
professional
success are
predicted by

4. Social skills training theories
••Behavioral shapingSkinner
••Psychotherapy by Reciprocal
Inhibition
Wolpe
••Assertion or assertiveness
training
Wolpe &
Lazarus
••Social learning theoryBandura

5. A classical social skills training
procedure
Assessment
Direct Instruction and Coaching
Modeling
Role Playing
Homework assignment & Follow-up

6. Role Playing
to practice the desired
behaviours in a
controlled setting
problems
••difficult
••expensive
Serious games

7. e.g. Scripted
based design
Social and communication skills
training & SG
Behavioural
oriented Serious
Games
Behavioural
oriented Serious
Games Design
Skinner
Wolpe
Wolpe &
Lazarus
Bandura

8. Behavioural oriented Serious
Games Design
Pros Cons
Knowledge
design and
reuse
The organization of the
interaction facilitates the
designof the scenario
hides the knowledge at its base
Interaction
with the
virtual agent
There is a fine control of the
scenario (e.g. the conversation)
The agent behaviour are
predetermined and the
interaction becomes repetitive
after few uses.
Player
Experience
Specific user's behaviours can be
trained
Players have no freedom. The
game experience is quite
different from a real one

9. Role of social context in
conversation in communication
The dialogue is a joint activity
that must consider both
individual and social processes
Different communication
strategies can be used
according to the specific social
context
The same sentence can be used
with a different meaning in
different context and can raise
different social effects
“You should take a cat”

10. A different approach to implement
the conversational agent: SALVE
Putting social practices at the heart of
the deliberation allows for more efficient
planning (Dignum and Dignum, 2014)
Social Agents for Learning in Virtual
Environments

11. The social practice model

12. Chatbot as a possible solution?
1966 – Eliza
1988 – Jabberwacky
1992 - Dr. Sbaitso
1995 - A.L.I.C.E.
2001 - Activebuddy’s Smarterchild
2011 - Watson, Siri
2012 - Google Now
2015 - Amazon Alexa , Microsoft Cortana
2016 – More than 18.000 Bots on Im, Messanger and Facebook

13. Chatbot as a possible solution?
Strength
• It is possible to quickly create a
conversational agent, avoiding
natural language processing
issues
• It is easy to define the chatbot
behaviour through the design of
proper question answers
modules (Alice -> AIML
categories)
Weaknesses
• Chatbots lacks the ability to keep an
overview and a structure of the entire
conversation.
• In AIML the dialogue is managed keeping
track of the last conversation exchange
and setting conversation topics.
• It is difficult to design chatbots able to
correctly manage social
conversational practices.
<category>
<pattern>MY NAME IS *</pattern>
<that>HELLO THERE WHAT IS YOUR NAME</that>
<template>Nice to meet you <star /></template>
</category>

14. Architecture of the SALVE system

15. Architecture of the SALVE system
Using chatbot just as an
interaction interface
Extend the AIML language that
describes the chatbot rules
with ”social” tags such that it
keeps track where it is in the
social practice (towards state
based dialogue)

16. Architecture of the SALVE system
Integrate chatbot with a rule
based engine (DROOLS) to
keep track of the agent states
and guide it the social
practice

17. How “social practice” guides SG
design

18. How “social practice” guides SG
design

19. How “social practice” guides SG
design
start end
greetings
present
Get
patient
data
Determine
symptoms
Determine
treatment
Communicate!

20. How “social practice” guides SG
design
start end
greetings
present
Get
patient
data
Determine
symptoms
Determine
treatment

21. Examples of S-AIML

22. Example rules: Timely greetings
lead to positive emotions
rule "GreetingsReceivedInTime"
when
$startScene:EnterScene(scene.name=="greetings")
$g:GreetingsReceived(this after[0ms,20000ms] $startScene )
then
controller.print($startScene.getScene().getName());
controller.print("greeting received in the first 20 seconds after the start of the scene");
OOCHappenedEvent he=new OOCHappenedEvent();
don(he,DesirableEvent.class);
don(he,ProspectedRelevantEvent.class);
insert(he);
controller.print("greeting marked as happened desirable prospected event");
insert(new ChangeOfSceneFromGoal());
end

23. Example rules: Timely greetings
lead to positive emotions
rule "GreetingsReceivedInTime"
when
$startScene:EnterScene(scene.name=="greetings")
$g:GreetingsReceived(this after[0ms,20000ms] $startScene )
then
controller.print($startScene.getScene().getName());
controller.print("greeting received in the first 20 seconds after the start of the scene");
OOCHappenedEvent he=new OOCHappenedEvent();
don(he,DesirableEvent.class);
don(he,ProspectedRelevantEvent.class);
insert(he);
controller.print("greeting marked as happened desirable prospected event");
insert(new ChangeOfSceneFromGoal());
end
rule "DesirableEventHappened"
when
OOCHappenedEvent(this isA
ProspectedIrrelevantEvent,this isA DesirableEvent)
$agent:Emotion(this isA Agent)
then
controller.print("captured desirable event");
$agent.setJoy($agent.getJoy()+1);
controller.print("increase joy");
controller.setJoy($agent.getJoy());
end

24. Example rules: Greetings not received in
time lead to negative emotions
rule "GreetingsNotReceivedInTime"
when
$startScene:EnterScene(scene.name=="greetings")
(not(GreetingsReceived(this after[0ms,20000ms] $startScene ))
then
controller.print("greeting not received in the first 20 seconds after the start of the
scene");
OOCNotHappenedEvent nhe=new OOCNotHappenedEvent();
don(nhe,DesirableEvent.class);
don(nhe,ProspectedRelevantEvent.class);
insert(nhe);
controller.print("dummy event marked as not happened desirable prospected event");
controller.respond("why you did not say hello!");
end

25. Example rules: Greetings not received in
time lead to negative emotions
rule "GreetingsNotReceivedInTime"
when
$startScene:EnterScene(scene.name=="greetings")
(not(GreetingsReceived(this after[0ms,20000ms] $startScene ))
then
controller.print("greeting not received in the first 20 seconds after the start of the
scene");
OOCNotHappenedEvent nhe=new OOCNotHappenedEvent();
don(nhe,DesirableEvent.class);
don(nhe,ProspectedRelevantEvent.class);
insert(nhe);
controller.print("dummy event marked as not happened desirable prospected event");
controller.respond("why you did not say hello!");
end
rule "DesirableProspectedEventNotHappened"
when
$d:OOCNotHappenedEvent(this isA ProspectedRelevantEvent,
this isA DesirableEvent)
$agent:Emotion(this isA Agent)
then
controller.print("captured not happened desirable event");
$agent.setDisappointment($agent.getDisappointment()+1);
controller.print("increased Disappointment");
controller.setDisappointment($agent.getDisappointment());
end

26. Empathic opportunities are given
and can be taken or ignored

27. SALVE architecture

28. SALVE architecture

29. SALVE architecture

30. SALVE architecture

31. Conclusion and future work 1/2
The proposed solution:
••puts social practice at the heart of the deliberative process of
an agent;
••allows for a dynamic activation of categories, depending on the
current social practice, the pursued plan, the on-going activity,
and finally, at the lowest level the agent’s identity;
••allows for a great flexibility in the conversation while at the
same time simplifying the formalization of the chatbot KB;
••ensures to the player a greater freedom in sentences
expression, and the possibility to experiment dynamic scenarios
and different roles;
••Lets the player actively create a conversation rather than
choose moves

32. Conclusion and future work 2/2
Future work:
••finalize the implementation of the
serious game according to a proper
learning design approach;
••Improve the social practices
representations;
••create a tool to support the designer
••validate the proposed approach