Presentatie IVA 2010 v01

8/2/2019 Presentatie IVA 2010 v01

1/24

Speed Dating with an

Affective Virtual Agent

Developing a Testbed for Emotion Models

Matthijs PontierGhazanfar F. Siddiqui

Johan F. Hoorn


2/24

Overview of this presentation

Background

About the model

The Speed-Dating Application

Results Conclusions

Future research


3/24

Background

Previously, we integrated 3 emotion models intoSilicon Coppelia, with the ultimate goal tocreate emotionally human-like robots

In simulation experiments, the system behaved

consistent with the theory it was based on, andseemed compelling intuitively

However, we tested our model using agentsinteracting with each other, not with a real user

Therefore we developed a speed datingapplication as a testbed for emotion models


4/24

The Models Incorporated in the Agent

I-PEFiCADM, a model for building agents that cantrade rational for affective choices based on theconcern-driven theory of Frijda.

EMA ,a model to create agents that exhibit andcope with (negative) affect based on Smith &Lazarus theory of emotion

CoMERG (the Cognitive Model for EmotionRegulation based on Gross), which can simulatedifferent emotion regulation strategies explained by

Gross using a set of logical rules and differenceequations.


5/24


I-PEFiCADM: Model to let agents trade rationalfor affective choices, based on theory Frijda


6/24


EMA: Model to let agents exhibit and cope with(negative) affect based on Smith & Lazarus theory


7/24


CoMERG: Can simulate different emotionregulation strategies explained by Gross


8/24

Combined model: Silicon Copplia

Simulate affective decision making process:decisions based not onlyon rationality, but alsoon affective influences

Simulate emotions based on beliefs about

world-states, and how these affect goals 5 emotions are simulated in parallel:

hope, fear, joy, distress and anger

Emotion regulation strategies can be applied toregulate (simulated) emotions


9/24

ENCODE COMPARE RESPOND

SITUATIONS FEATURES APPRAISAL APPRAISAL RESPONSEDOMAINS PROCESS COVERT OVERT

Integratedmodel

currentvalence

weighted

features

ethicsaffordances

aestheticsepistemics

currentstate

predicates

futurestate

predicates

affectivestates

emotionsmood

Features are matched against goals, concerns, beliefs, intentions, etc. of self and others (allows taking perspectives)

involvementdistance

satisfaction

situationmodification

futurevalence

relevance

affectivedecisionmaking

situation selection

pos

itiveapproach

neg

ativeapproach

cha

nge

avoid

experiential behavioral

physiological(e.g., arousal, heart-beat,., sweat, blush)

response modulation

cognitive change

attentional deployment

similarity

Relevance and valence (gray area) are the variables in the appraisal frames

use intentions

appraisal

frames

User

feature

Affords Robot goal

state

Positive /

Negative

Facilitates

/ Inhibits

Desired /

Undesired


10/24

Determining which action to take

Agent has beliefs that actions facilitate goals (get date/ be honest / connect well with user), and aboutgeneral positivity and negativity of certain actionsConversation tree

Perceived Ethics of the user is updated using Positivityand Negativity of user responses

Perceived Aesthetics of the user are updated usinguser responses during the conversation topicAppearance

Perceived Affordances of the user are updated usingthe Expected Utility of the user responses
http://www.few.vu.nl/~ghazanfa/IVA2010/Interactions_800_graph.pdfhttp://www.few.vu.nl/~ghazanfa/IVA2010/Interactions_800_graph.pdfhttp://www.few.vu.nl/~ghazanfa/IVA2010/Interactions_800_graph.pdf


11/24


Bias for perceiving ethics is updated usingBelieved responsibility for reaching goals

For actions that facilitate a desired goal, ahigh Action_Tendency is generated


12/24


Expected Satisfaction is calculated using thefollowing formula, and the action with thehighest level of expected satisfaction is picked

ExpectedSatisfaction(Action) =

wat * Action_Tendency +

wpos * (1 - abs(positivity biasI * Involvement))+

wneg * (1 - abs(negativity biasD * Distance))


13/24

Calculating Hope and Fear

Hope and fear are based on perceivedlikelihood [0, 1] of goals

Higher hope_for_goal if likelihood close to f

f is set to 0.5 Hope for negative ambition_level is fear

IF f >= likelihood THEN hope_for_goal =

-0,25 * ( cos( 1 / f *p

* likelihood(goal) ) -1,5) * ambition_level(goal)

IF f < likelihood THEN hope_for_goal =

-0.25 * ( cos( 1 / (1-f) * p * (1-likelihood(goal)) ) -1.5) * ambition_level(goal)


14/24

Calculating Hope and Fear

The following algorithm is performed:1. Sort hope_for_goal values in two lists: [01] and [0-1]

2. Start with 0 and take the mean of the value you have and thenext value in the list. Continue until the list is finished. Do this

for both the negative and the positive list.3. Hope = Outcome positive list.

Fear = abs(Outcome negative list).

This way, multiple hope_for_goals increase hope. However, with the more hope_for_goals there are, the

less impact each extra hope_for_goal has on the levelof hope


15/24

Calculating Joy and Distress

Joy and Distress are based on (not) reaching(un)desired goal-states:

Reaching desired goal increases joy, decreases distress

Reaching undesired goal decreases joy, increases distress

Higher ambition level goal Bigger impact on emotions

IF ambition_level(goal) >= 0 THEN:

new_joy = old_joy + mf_joy * ambition_level(goal) * (1-old_joy)

new_distress = old_distress + mf_distress * -ambition_level(goal) * old_distress

IF ambition_level(goal) < 0 THEN:

new_joy = old_joy + mf_joy * ambition_level(goal) * old_joy

new_distress = old_distress + mf_distress * -ambition_level(goal) * (1-old_distress)


16/24

Calculating Anger

Anger agent is calculated using believed responsibilityof user for success speed date

IF Belief(HumanResponsible) * Ambition > 0

THEN Anger(Agent) = old_anger + mfanger *(-Belief(HumanResponsible)) * Ambition * (1 - old_anger)

IF Belief(HumanResponsible)) * Ambition(Goal) < 0

THEN Anger(Agent) = old_anger + mfanger

*

(-Belief(HumanResponsible)) * Ambition * old_anger

Anger is multiplied with a decay factor each step


17/24

The Speed-Dating Application

User converses with the agent named Tom byselecting a response from the drop-down box

All 5 emotions are simulated in parallel, and areshown by the facial expression of the agent

Agent picks its response based on affectivedecision making Silicon Coppelia


18/24


19/24

The Experiment

Participants (all female students) were told they werecommunicating with a male student via this application

A 94-item questionnaire was developed to measurevariables. Items were measured on a 7-point Likert scale

H1: Users recognize a direct positive effect of agent-assessed Aesthetics on the agents Involvement with theuser

H2: Agent-assessed Ethics of the user has a positivedirect effect on Use Intentions of the agent to meet theuser again

H3: Relevance of user behavior to agent concerns has amediating effect on the relation between agent-assessedEthics and Use Intentions (see H2)


20/24

Results

H1 was confirmed: Aesthetics was significantlypositively correlated to Involvement (p < .01)

H2 was confirmed: Ethics was significantly positivelycorrelated to Use Intentions (p < .05)

H3 was rejected: the mediating role of Relevance wasnot found to be significant (p = .24)


21/24

Discussion & Conclusions

Users recognized that Tom became moreinvolved when he found them attractive

Users recognized that Tom was more

inclined to meet them again, when hethought they were morally good, althoughthe mediating effect of Relevance was notfound to be significant


22/24

Discussion & Conclusions

We developed a speed dating application asa testbed for emotion models

We tested Silicon Coppelia by letting users

interact with the speed-dating agentequipped with this model

Other emotion models can be easily

connected to the application The application can easily be adjusted to let a

human control the speed dating agent, whichenables doing Wizard of Oz studies


23/24

Future Work

Compare the performance of models suchas I-PEFiCADM, EMA, and Gross with aWizard of Oz condition of human-human

interaction


24/24

Questions?

Presentatie IVA 2010 v01

Documents

Transcript of Presentatie IVA 2010 v01