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Embodied Conversational Agentsand
Affective Computing
EASSS 2012
http://acai.lip6.fr/
Alexandre Pauchet [email protected] Sabouret [email protected]
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http://acai.lip6.fr/
French working group ACAI
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GT ACAI
2004: GT ACA (ECA) 2012: GT ACAI
GDR I3, SMA from AFIA Affects, Artificial Companions and Interaction
Thematic meetings Corpus Virtual agents Affective computing ....
From 10 to 30 people
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GT ACAI
Workshop WACA 2005, 2006, 2008, 2010 Chatbots, dialogical agents Assistant agents Virtual agents Emotions
WACAI 2012 in Grenoble Affects, Artificial Companions and Interaction
Web Site: http://acai.lip6.fr/ GT ACA: http://www.limsi.fr/aca/
Mailing list: [email protected]
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Plan
Embodied Conversational AgentsIntroduction to ECAs, talking heads, gestural agents,
deploying environments, assistant agents, usefulness of embodiment, human-ECA interaction
Affective ComputingIntroduction to affective computing, affective models,
ECAs and emotions
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Embodied Conversational Agents
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Introduction
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Definitions
ECA : "Embodied Conversational Agent"IVA : "Intelligent Virtual Agent"
Agent
Conversational
Embodied
Perception (awareness)
Decision(rationality, AI)
Expression(affects)
Agents La Cantoche™ Aibot Sony™
Rational Proactivity
Multimodal interaction Social
Personification Situated
Multimodality- Graphic User Interface (GUI)
- Natural Language Processing (NLP)...
Microsoft™
Icons Virtual Characters Robots
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Roles of ECAs
ECAs are “Interactive Virtual Characters” that are situated in mediated (often distributed) environments.
They can play four main roles:
Assistants to welcome users and to assist them in understanding and using the structure and the functioning of applications
Tutors for students in human-learning mediated environments, or for patients in psychological/pathological monitoring systems
Partners for actors in virtual environments: partner or adversary in a game, participant in creative design groups, member of a mixed-initiative community, …
Companions as a friend in a long term relashionship
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Talking heads
Fixed – Realistic
Expressions – LipSynch
Emotions
Gestural Agents
Fixes/floating – moving arms
Dialogue – Deictic – Sign language
Tutoring – Assistance
Situated Agents
Complete mobile characters
Virtual/Augmented reality
Training – Action
GRETA (Pélachaud LTCI) STEVE (Rickel et al USC) VTE (Gratch et al USC)
Categories of ECAs
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© CANAL+
APPLICATIONS WEB PAGES AMBIENT
Educ
atio
nA
ssis
tan c
e
Mix
ed
Com
mun
itie
sW
elco
min
g
Vir
tual
Rea
lity
Smar
t O
bjec
ts
Deploying environments
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Agent models: interaction performance/efficiency of models Interaction models with users: Multi-modality, H/M Dialogue
Models of cognitive agents: BDI logics and planning, affective logics, ...
Task models and user models: Symbolic Representation and Reasoning
Human modelling: ecological relevance of models Capture
Representation
Reproduction
Evaluation
Application
of physiology
of expressions
of behaviours
of humans
Scientific issues
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Computer Science and Natural Language Processing Multi-Agent Systems
Human-Machine Interaction/Interfaces
Human-Machine Dialogue
Humanities and Society Sciences Psychology of Language Interaction
Ergonomic psychology
Scientific communities
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Projects and teams
European project SEMAINE
(http://www.semaine-project.eu/)
LTCI (France): Greta
LIMSI (France): MARC
Bielefield University (Germany): MAX
USC (USA): STEVE, VTE
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Independent of the embodiment level
Non verbal behaviours (postures, gestures, ...):
Theoretical approach (related works) Empirical approach (corpus analysis)
Generated animations
From an intention (triggering action) Automatically and cyclically
Design of animated characters(ECAs or avatars)
<configuration> <timecode>10</timecode> <body>front</body> <head>front</head> <eyes>open happy</eyes>… … … <leftArm>hello</leftArm> <rightArm>hip</rightArm> <speech>Hello!</speech></configuration>
Corpora Representation Reproduction
Evaluation
Userfeeling?
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Nikita by Kozaburo © 2002
Done with Poser 5 / No PostworkModel: DAZ-3D Victoria-3Skin Texture:Mec4D
Beauty contest for Embodied Conversational agentswww.missdigitalworld.com
Talking heads
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G. Bailly and F. Elisey, GT ACA, 2006.03.15, http://www.limsi.fr/aca/
Visem: elementary unit of the position of the muscles of the human faces (3D model)
Experimental settings: Small red balls manually placed on the face 3 to 5 cameras Training corpus
Models based on visems
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Torsion model of the face and the neck
The positions of the red balls are set manually
Pattern recognition is used to monitor ball movements
Extraction of key frames automatic and manual
Transitions between expressions
G. Bailly and F. Elisey, GT ACA, 2006.03.15, http://www.limsi.fr/aca/
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Analysis and synthesis
Visems + 3D model are used to (re-)generate expressions
6 to 11 parameters
Validation by comparing to real expressions
G. Bailly and F. Elisey, GT ACA, 2006.03.15, http://www.limsi.fr/aca/
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‘Talking head’ project (LIMSI)
Based on visems Video corpus of 21
utterances:
... C'est u
i ci ...
« C'est /phonem/ ici » (Ex: « C'est u ici »)
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Greta (Prudence, Obadiah, Poppy and Spike)LTCI – Telecom ParisTech
MARCLIMSI
Realistic talking heads
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http://marc.limsi.fr/
Gestural agents
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Design of realistic gestural agents and avatars
Annotated corpus of videos (ex: Anvil)
Psychological knowledge (Related works)
MARC (LIMSI)
GRETA (LTCI)
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LEA Agents (LIMSI)• 2D cartoon characters (110 GIF files)• Easily integrated into Java applications and JavaScript• Description language for high-level behaviours
Lea Marco Jules Genius
2D cartoon agents: Lea
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deictic gestures
iconic gestures adapters Emblematic gestures
…
…
transparent GIF files
Lea: gestural expressiveness
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<?xml version='1.0' encoding='utf-8'?>
<configurationsequence nbrconfig="1">
<configuration> <timecode>10</timecode>
<body>front</body> <head>front</head> <eyes>open happy</eyes> <gaze>middle</gaze>
<facialExpression>close</facialExpression>
<bothArms>null</bothArms> <leftArm>hello</leftArm> <rightArm>hip</rightArm> <speech>Hello!</speech>
</configuration></configurationsequence>
Number of configurations
= attitudes
An
att
itu
de
Specifying attitudes and animations in XML
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Tabl
e :
F réd
éric
Ver
nier
LIM
SI-C
NR
S
Dynamic Deictic
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GLA-TEK-ZIM
KUB-OLO-ZIM
Designation of the DOM objects
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APPLICATIONS
WEB PAGES
AMBIENT
Deploying environments
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Chat bots in the Internet
Chat bot = “chatterbox” + “robot” A program capable of NL dialogue
Usually key-word spotting No dialogue session or context
No embodiment Goals: only chat
ELIZA (J. Weizenbaum,1965)
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Assistant agents (application/web)
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Social avatars on the web
Social avatarsThe recent evolution of the communication over the web has prompted the development of so-called “avatars” which are graphical entities dedicated to the mediating process between humans in distributed virtual environments (ex: the “meeting services”)
Contrary to an ECA which is the personification of a computer program (an artificial agent) an avatar is the personification of a real human who controls the avatar.
(Avatar = Mask) ≠ Agent
Second lifePresented as a « metaverse » by its developer, Linden Lab, Second Life is a web-based virtual simulation of the social life of ordinary people.
5 000 000 avatars were created Permanently , ~ 200 000 visitors are logged
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Mixed communities
Project : « Le deuxième Monde » 1997-2001• Company: Canal +™• Head: Sylvie Pesty (LIG – Grenoble)• PhD: Guillaume Chicoisne (LIG – Grenoble)
Mixed Community• People chatting and purchasing in a Virtual World• ECA interacting with the people
Embodied Conversational agent
Avatars of users
© CANAL+
© CANAL+
Navigation and chat interface
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Metaphor: meetings
Definition : computerised environments that integrates transparently humans and artificial agents within meetings
Mixed initiative communities: brainstorm, design, decision, …
Virtual Training Environments (VTE) : training, simulation, …
Deploying environments
Smart Room: physical place with augmented reality
Web-based : distributed and mediated environments
Animated characters
Avatars : virtual characters driven by humans
Agents : virtual characters which embodies artificial agents
Mixed communities andHeterogeneous MAS
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Mixed training environments Avatars ECAs
Task: virtual firemen Recognition of fire Activity reports
Interaction Emotion management Gestures and facial expressions NL dialogue management
M. El Jed, N. Pallamin, L. Morales, C. Adam, B. Pavard, IRIT-GRIC, GT ACA, 2005.11.15 — www.irit.fr/GRIC/VR/
+
Social interaction in virtual environments
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The study of videos enabled to highlight how deictic gestures and gazes towards items are used concomitantly with natural language words ("here", "it", "now", ...)
M. El Jed, N. Pallamin, L. Morales, C. Adam, B. Pavard, IRIT-GRIC, GT ACA, 15 nov 2005 — www.irit.fr/GRIC/VR/
Realistic behaviour of agents
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From assistant agents to ambient environments
Transporting the Function of Assistance from stand-alone applications to room-based ambient environments
Application taken from the DIVA toolkit (LIMSI)
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DIVA display devices of the HD TV Screen Large touch Screen Portable touch Screen
Presentation of any Internet content Presentation of the IRoom controls
Diva Toolkit + IRoom (LIMSI)
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DIVA 3D-pointing gestures9 loci
Diva Toolkit + IRoom (LIMSI)
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Pointing objects in the physical world
“Which of the two remote controllers should I use?”
Body zooming for close objects
Diva Toolkit + IRoom (LIMSI)
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Assistant agents
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Examples from professional web sites
Question
ECA
EDFSite
P. Suignard, GT ACA, 2004.10.26http://www.limsi.fr/aca/
Lucie, virtual assistant of SFR
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A cons-example: the « Clippie effect »
Microsoft Agents™― Free technology – no support
― Agents 2D cartoon
― Can be used on web pages
― Third part characters (LaCantoche™)
― API for JavaScript & VBScript
Limitations― Inputs limited to clicks and menus
― No proper dialogue model
― No application model
― No synchronization speech/movements
― Emotions = cartoon ‘emotes’
Intuitive request expressed in NL
2 results too far ranked but quite relevant
Helping text corresponding to
the 1st line of result
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« An Assistant Agent is a software tool with the capacity to resolve help requests, issuing from novice users, about the static structure and the dynamic functioning of software components or services »
User person with poor knowledge about the component (novice)
Request help demand in natural language (speech/text)
Component computer application, web service, ambient appliance
Agent rational, assistant, conversational, (can be embodied)
Mediator symbolic model of the structure and the functioning
InterViews Project – February 1999Following Patti Maes MIT, 1994
Assistant agents: definition
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Assistant agent
Novice user
Symbolic model
Software component
The assistant metaphor
Request
???
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Human Agent
Software Agent
Modeling Agent
Rationall Agent
Linguistic Agent
Assistant ECA
Exemple : un groupe nominal
§Gboxanalysis"le tres petit bouton rouge"GRASP PHASE 1 RULELIST length 74
GRASP PHASE 2 RULELIST length 9
FLEX leLEM lePOS DDKEY THERTYPE LEMID 54252
FLEX tresLEM tresPOS RRKEY INTLARGERTYPE LEMID 54253
FLEX petitLEM petitPOS JJKEY SIZESMALLRTYPE LEMID 54254
FLEX boutonLEM boutonPOS NNKEY BUTTONRTYPE LEMID 54255
FLEX rougeLEM rougePOS JJKEY REDRTYPE LEMID 54256
HIT: RR: R J tres , petitFLEX leLEM lePOS DDKEY THERTYPE LEMID 54252
FLEX petitLEM petitPOS JJKEY SIZESMALLRMODE :
FLEX tresLEM tresPOS RRKEY INTLARGERTYPE LEMID 54253
SETK 1TYPE LEMID 54254
FLEX boutonLEM boutonPOS NNKEY BUTTONRTYPE LEMID54255
FLEX rougeLEM rougePOS JJKEY REDRTYPE LEMID 54256
HIT: NN: D J NE J le, petit , bouton , rougeFLEX boutonLEM boutonPOS NNKEY BUTTONRDET
FLEX leLEM lePOS DDKEY THERTYPE LEMID 54252
ATTR :
FLEX petitLEM petitPOS JJKEY SIZESMALLRMODE :
FLEX tresLEM tresPOS RRKEY INTLARGERTYPE LEMID 54253
SETK 1TYPE LEMID 54254
FLEX rougeLEM rougePOS JJKEY REDRTYPE LEMID 54256
SETK 2TYPE LEMID 54255
Σ Hi
Comment je peux …
Hello !
MODEL[ ID[main] PARTS[ TITLE, FIELD, GROUP[ ID[command], PARTS[ BUTTON, BUTTON, BUTTON ] ] ], USAGE["This
component is …" ]
]
Dialogue Session
Profil
The agents of the Daft project
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Formal answer language
Interaction model
- Session- Profile
- Task
Rational agent
The agent reasons,
modifies the models
Σ Hi
ASK[COUNT[REF(BUTTON)]]
« How many buttons are there? »
TELL[EQUAL[COUNT[REF(BUTTON)]],3]]
« There are three buttons »
Loop1. Read a formal request ;
2. Contextualize the formal request: - Explore the interaction model; - Explore the component model.
3. Process the formal request : - Update the interaction model; - Update the component model.
4. Produce the formal answer.
Component model
- Structure
- Action- Process
Formal request language
The Rational Assisting Agent
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Here is …
Standard GUI that can contain Java applets
“Counter” component
“Hanoi” component
“ AMI web site” component
...
LEA
counter
Speech synthesis: Elan SpeechACA LEA (Java)
J-C Martin & S. Abrilian
Java Interface for ECA: DaftLea (K. LeGuern 2004)
Dialogue with components
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«restart the counter »
When the user enters « restart the counter » in the chatbox, he/she can see:
- The number increments 8, 10, 12, 14 …- LEA says: « Done! »- LEA expresses the emote: ACKNOWLEDGE
Done!
1) The utterance is analyzed to build a formal request: RESTART[REF[“COUNTER”]]2) The reference REF is resolved within the model (large red rectangle)3) The restart action is applied on the boolean of the counter thus producing the model variable INT[8] to be incremented4) An update is sent to the boolean variable of the application5) The behavioral answer « ACKNOWLEDGE » is sent to the virtual agent.
12
3
4
MEDIATING
MODEL
Processing of a Daft request
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LIMSI : Diva/DivaLite
Signing agents
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Usefulness of embodiment
3D realistic agents
2D cartoon
Eyeballs
Humans
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Efficiency Measures the actual performance of the couple user-agent when carrying out a given task.
Usability Measures the capacity of the user to have a good comprehension of the functioning of the system and the fluency of the control.
Friendliness Measures the “feeling” of the user about the features of the system (attraction, commitment, esthetic, comfort, …)
Believability Measures the “feeling” of the user about the fact that the agent can understand the user’s problems and has the capacity to help with real competence.
Trust Measures the “feeling” of the user about the fact that the agent behaves as a trustable and cooperative entity.
Ob
ject
ive
Su
bje
ctiv
e
Evaluation of ECAs
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% F
rien
d lin
ess
% similarity
Toy"uncanny
valley"
Bunraku
puppet
Masahito Mori, Institut Robotique de Tokyo in Jasia Reicardt, “Robots are coming”, Thames and Hudson Ltd, 1978
3D realistic agents2D cartoonEyeballs Humans
Ratio realism / friendliness
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WITH AGENT WITHOUT AGENT
30 Sujects : 15 M & 15 F,
age ≈ 28 years
PresentationWith Agent
PresentationWithout Agent
Measured improving of the parameters:- performance of comprehension- performance of recall (memory)- subjective questions satisfaction
• Lester et al. (1997). The Persona Effect: Affective impact of Animated Pedagogical Agents. CHI’97. • van Mulken et al. (1998). The Persona Effect: How substantial is it? HCI’98.
Results
The Persona Effect of Lester
ECAAn arrow
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61 Sujects :39 M, 22 F
Measured improving the performance of recall of stories
Subjective evaluation:Anthropomorphic questions
Example : “Did you feel the agent sensitive?”
Experiment: stories are told to subjects using three various situations (without agent, realistic agent, cartoon agent). Recalling questions are then asked to subjects. The three situations are compared.
Result: an ECA improve the memorizing of information.
Ik ben Annita, de assisterent bj dit experiment
Realistic agent
Without agent
Cartoon agent
Beun et al. (2003). Embodied conversational agents: Effects on memory performance and anthropomorphisation. IVA’03.
Persona effect and memorizing
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Experiments on Functional description
Three different agents: ― 2 men (Marco, Jules) with different garment― 1 woman (Lea)
Three strategies of cooperation: ― Redundancy ― Complementarity ― Specialization
Three objects to describe:1. Video recorder remote controller 2. Photocopier control panel3. Application for designing graphic documents
33 = 27 experiments
1
23
Stéphanie Buisine, LIMSI 2004
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Functional description: evaluation results
Quality of explanation―No noticeable effect of the appearance,―Noticeable effect of the multimodal behaviour:
the redundant explanations are preferred.
Sympathy―No noticeable effect of the multimodal behaviour,―Noticeable effect of the appearance :
Performance―Same as the appearance,―But no correlation between Sympathy and
Performance.
Impact of the user’s gender―Men react differentially and prefer redounding
explanations,―Women do not react differentially.
> >
Stéphanie Buisine, LIMSI 2004
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Human-ECA Interaction
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Turing test: conversational intelligence
Turing Test, Alan M. Turing (1912-1954) : "the imitation game" in “Computing Machinery and Intelligence”, Mind, Vol. 59, No. 236, pp. 433-460, 1950.
Can be replaced by a Turing-like test, on output data instead of during direct interaction
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Wizard of Oz methodology
Wizard of Oz An experimenter pilots an avatar The user ignores is in front of a human (he thinks
he faces a virtual agent)
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Humain-Agent interaction
Interaction
InteractionInteraction
Multiple inputsTextual (text boxes), audio (microphone), video (camera), sensors (AmI environment),...
Multiple outputsTextual, audio, facial expressions, postures, gestures, lighting, ...
Multiple modelsConversational, emotional, of the environment, of the user, ...
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European project SEMAINE:an ECA architecture
http://www.semaine-project.eu/
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Example of ECA architecture
http://semaine.opendfki.de/wiki/ComponentArchitecture
Semaine Project
Spike
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Interaction inputs
Camera
Microphone
Sensors
Text box
Microphone + transcript / text box: dialogue input (semantic)
Microphone (emotion) Camera:
Gestures (semantic) Facial expressions / postures (emotions)
Sensors: Context Physiological signals (emotions)
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Human-ECA Dialogue
S. Pesty, GT ACA, 2005.11.15, http://www.limsi.fr/aca/
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Textual / GUI Embodied
Non linguistic • Contextual help: pop-ups, ...• Online manuals: hypertext navigation• Assistants using a decision tree
• State printer: emotion, emotes
Unidirectional, linguistic
INPUT• Query fields embedded in an application window: keywords, free/open questions
SORTIE• Feature presenter
Conversational (bidirectional, linguistic)
• Chat bots: Eliza• Dialogical assistants:
InterViews, Daft, Artemis, …
• Mediators: acts as a representative of an application of a service• Avatars
Conversational agents
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The reference of the chatbotsA. L. I. C. E.
Author: Richard S. Wallace Website: http://www.alicebot.orgDocumentation : http://alicebot.org/TR/2001/WD-aiml-1.0.1-20011025-006.html
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Jabberwacky wins the Loebner Prize 2005
….PROGRAM: Hello, my name is George and I am the human.JUDGE: Hi George, cslkjslw weicmslw weos, right?PROGRAM: Don't repeat your name, csthiang!JUDGE: If lsjkel lwehlx wouldn't you say?PROGRAM: No, he's still good.JUDGE: Who.PROGRAM: You and me.JUDGE: If you are human, tell me which is larger, a salt crystal or the Empire State Building.PROGRAM: It's caring about another person's well being more than your own.JUDGE: What if I care about another person's well being more than I should?PROGRAM: How would you do that?JUDGE: Which is further away from us, Queens or Inner Mongolia?PROGRAM: HA! HA! That was stupid. Tell me a GOOD joke this time.JUDGE: You say you are human but you can't answer a simple question.PROGRAM: Because I am alive.JUDGE: You are not following the rules. You are supposed to convince me that you are a human, not convince me that you are mysterious.PROGRAM: I can't very well convince one lacking a mind, can I?JUDGE: It isn't my mind that is in question, it is yours!PROGRAM: No my name is not katie.JUDGE: Which is larger, your brain or Belgium?PPROGRAM Oh the one on the far left without a doubt.JUDGE: Francais.ROGRAM: Yes do you speak it?JUDGE: Francais.PROGRAM: Francais?JUDGE: Francais.PROGRAM: O well I don't speak french.JUDGE: Who.PROGRAM: Mine.JUDGE: The ljwli is what we want, right? PROGRAM: I can not do it right now because I m talking to you a million miles away.JUDGE: What do you think the ljwli.PROGRAM: Depends on the person.JUDGE: But the ljwli.PROGRAM: So they can chuck more wood?…
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Dialogue systems
Retro-action loop not taken into account: knowing the state of the dialogue (DM) can improve
Automatic speech recognizer
Natural language understanding
The DM should manage multimodality
As input (ex: gestures, facial expressions, emotions)
As output (ex: emphasis, voice modulating, utterance+gesture)
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Utterance understanding
Speech act theory (Searle&Vanderveken, 1985) In accordance with Bratman's theory of
intention (Bratman, 1987) Illocutionary acts (Vanderveken, 1990) Formalisation: F(P) 5 speech acts: assertive, commissive,
directive, déclarative, expressive Transposition to MAS
KQML FIPA ACL
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Existing dialogue systems
Case-base systems using keyword spotting: chatbot
Communication protocols (FIPA ACL): MAS
Planning systems (Allen 80, Traum 96)
POM-DP: call centres (Frampton&Lemon 09)
Dialogue games (Maudet 00)
Information-State update (Larsson 00) : Trindikit, GoDiS, IbiS
Scientific deadlock yet unsolved!(see Mission Rehearsal Exercise (Swartout et al. 2006))
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Ratio Effort/Performance
Effort = Code and resources
Actual Perform
a nce
1 10010 1000
Chat bots
H/M Dialog Systems
10%
50%
100%
Games, socialization, games, affects, …
Control, command, assistance…
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Affective Computing
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Kesako ? Affective Computing
● Book by R. Picard (1997)● Detect, Interprete, Process and Simulate
human emotions
How is it conncted to ECAs ?● (Krämer et al., 2003)
People, when interacting with an ECA, tend to be more polite, more nervous and behave socially
● (Hess et al., 1999)Emotions play a crucial rôle in social interactions
→ Affective Conversationnal Agents !
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The story so far...
H-M Dialog
Virtual Agents
Eliza Trains
Online ECAseverywhere
70 90 10
Emotions
Video Games Serious GamesSimulation
R. Picard Humaine
IVA
ACII
Agents MAS
CHI
AAMAS
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Links from the community
IVA (Intelligent Virtual Agents)
http://iva2012.soe.ucsc.edu/
ACII (Affective Computing & Intelligent Interaction)
http://www.acii2011.org/
Humaine● Projet FP6 (1/12004 – 31/12/2007)● Association● http://emotion-research.net/
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HUMAINE
http://emotion-research.net
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Research questions
Detect, interprete, process and simulate human emotions → affects
Cognitive Sciences● AI and psychology (for emotions)● AI and sociology (for social relations)
3 domains :
Recognition Models Synthesis
Patterns Reco AI Image
Signal processingFusionMachine Learning
Formal Models of InteractionsKR & reasoningBehaviour models
SynchronisationTurn taking
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Examples
Recognition● Affectiva Q sensor :
http://www.youtube.com/watch?v=1fW3hEvxGUU
Synthesis● Agent :
http://www.youtube.com/watch?v=CiuoBiJjGG4
● Robot (Hanson) : http://www.youtube.com/watch?v=pkpWCu1k0ZI
Models● Fatima (LIREC) :
http://www.youtube.com/watch?v=ZiPlE80Xiv4
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Emotion recognition General principle
● Corpus tagging● Supervised learning (classifier)
Corpus(images, films...)
Annotations
E.g. Anvil
Source
Taggedcorpus
Emotion categories
SupervisedLearning
Decisiontree
classes
criterions
E.g. ID3
Durations, Pitches,Frequencies, Colors,
Etc...
Tags
Extraction
Emotion
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Multi-modal behaviour annotation: ANVIL
Outil ANVIL
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Emotions synthesis
From Emotion to Parameters
Evaluation● Human recognition of expressed emotions
not reliable, even in H-H interaction (with spontaneous emotions)
Some examples:● Virtual agents: Greta/Semaine● Robots: Kismet, iCat, Nao
Emotions
parametrizationExpressing
AnnotatedCorpus
State transitionsDynamicsGeneralization
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From annotations to animations
OriginalOriginalvideovideo
AngerAnger DesperationDesperation
C. Pélachaud LTCI and J-C Martin LIMSI
GRETAGRETA
JoyJoie
AngerColère
FearPeur
SurpriseSurprise
DistressTristesse
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Affective Models
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A bit of Human Science
Darwin, 1872 (Ed. 2001)The expression of emotions in man and animals
→Adaptation mechanism
+ Role in communication (Ekman, 1973)
2 schools● James, 1887
Organism changes → emotion● Lazarus, 1984 ; Scherer, 1984
Appraisal theory
« The question is not whether intelligent machines can have any emotion, but whether machines can be intelligent without any emotions. »
Marvin Minsky, 1986
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Appraisal theory
Environment
Individual
Perceptions= stimuli
Goals, preferencesPersonnality
Emotion
Adaptation
aka coping
Actions
Evaluation in context
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Appraisal theory (2)
Coping (adaptation strategy)= the process that one puts between
himself/herself and the punishing event● Active coping (i.e. problem focused)
→ act upon one's environment● Passive coping (i.e. emotion focused)
→ act upon one's emotion
Rousseau & Hayes-Roth, 1997● Personnality → emotions (Watson & Clark, 1992)
● Emotions → Social relations (Walker, 1997)
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Architecture
Events
Emotions
Personnality
Socialesrelations
Actions
Social role
Mood
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Architecture
Events
Emotions
Personnality
Socialesrelations
Actions
Social role
Mood
1
2
3
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Models of personnality
Personnality = stable component in an individual's behaviour, attitude, reactions...
Computational models● Define variables● Define their value domains● Define the characteristic values
Eysenck, 1967● Extraversion → Higher sensitivity to positive
emotions (and more expressive)● Neuroticism → Higher sensitivity to negative
emotions (and more frequent changes)
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Models of personnality (2) McRae, 1987 (a.k.a OCEAN or « Big Five Model »)
● Openness → curiosity● Conciousness → organization● Extraversion → exteriorization● Agreeableness → cooperation● Neuroticism → emotional unstability
Myer-Briggs Type Indicator (MBTI) based on Jung
● Energy → introverted or extroverted● Information collection → sensitive or intuitive● Decision → thinking or feeling● Action → judgement or perception
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One example: ALMA(Gebhard, 2005)
OCEAN
Events
Emotions
Mood
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Emotions models
What is an emotion ?● Darwin, James, Ekman, Scherer, …
Characterizing an emotion● Categories-based approaches
→ There are N emotions (N being subject to discussion)
● Dimensions-based approaches→ An emotion is a point in an N-space (dimensions
must make a basis)
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The PAD model
Mehrabian, 1980 Emotion = 3 dimensions
● Pleasure (or « valence »)● Arrousal (or « activation degree »)● Dominance
Examples
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PAD (cont.)
Pluchnick's wheel
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The OCC model
Ortony, Clore, Collins, 1988 20 emotion categories
● Joy – Distress● Fear – Hope● Relief – Disappointment, Fear-confirmed –
satisfaction● Pride – Shame, Admiration – Reproach● Love – Hate● Happy-for – Resentment, Gloating – Pity● Remoarse – Gratification, Gratitude – Anger
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OCC (cont.)
Appraisal model based on individuals' goals & preferences:
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One example: EMA
EMotions & Adaptation
→ Appraisal
→ Coping (action selection)
Based on SOAR Evaluation criteria
● Relevance● Point of view● Desirability (expected utility)● Probability & expectedness● Cause● Control (by me and others)
(Gratch & Marsella, 2010)
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EMA (cont.)
Rule-based systemE.g. : Desirability(self, p) > 0 & Likelihood(self, p) < 1.0 → hope
Coping strategies● Perceptions● Belief revision (including about other agents'
intentions and responsibilities)
● Changing goals
Simulation model Application to serious
games
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Social relations
Social behaviour model
Static models: Walker, Rousseau et al., Gratch...
Dynamic model: Ochs et al.
Several traits:● Liking● Dominance● Solidarity (or social distance)● Familiarity (subject to discussion)
→ Social Relation unidirectional and not-always reciprocal!
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Emotions and Social Relations
Influence E → RS (examples)
● Ortony, 91 : caused >0 emotions → + liking● Pride, Reproach → + dominance
Fear, Distress, Admiration → - dominance
Influence of the social relation on action and communication
Action selection guided by target social relation Emotional contagion
→ neighbours computation based on social relations
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One example : OSSE(Ochs et al., 2008)
Scenario= set of events
Attitudes
Events→ Emotions→ Social relationdynamics
Social roles→ initial relation
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Learn more !
Books● Rosalind Picard, Affective Computing, MIT
Press● Scherer, Bänziger, & Roesch (Eds.) A blueprint
for an affectively competent agent: Cross-fertilization between Emotion Psychology, Affective Neuroscience, and Affective Computing. Oxford University Press
Next IVA conference → next September
humaine-news mailing list !
ACII in 2013
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Acknowledgements
Jean-Paul Sansonnet (LIMSI) for his presentation materials
All members of GT ACAI
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Join theGT ACAI!
Conclusion
http://acai.lip6.fr/