BRIMS ‘05
The “Etiquette Quotient”:
An Approach to Believable Social
Interaction Behaviors
Christopher A. Miller
Marc Chapman
Peggy Wu
Lewis Johnson
Simulations for Cross Cultural Training
Military forces increasingly need training in the cultures
they deploy to (esp. for urban ops)
Cross Cultural training helps, but is a huge drain on
resources and time
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200 soldiers trained for Iraq deployment (Mares, 2003)
Soldiers willingly spend off-duty time playing PC games
Solution: Make games that teach elements of culture
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Estimated DoD-wide training savings of >$1B/year (Chatham,
2003)
Problem: Avatar behaviors are hand-scripted, hence
brittle and unbelievable, impossible to change culture
Solution: A general, computational social interaction
model with “pluggable” culture knowledge modules
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Approach Summary
We know that humans react socially (according
to human conventions) to complex automation
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Reeves and Nass
We have access to a theory/model of how
humans decide what politeness behaviors to
exhibit
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Brown and Levinson
We have evidence that the model predicts human
reactions to human-machine interactions
So let’s develop a metric of believable politeness
based on Brown and Levinson
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Reeves and Nass
Reeves, Byron & Nass, Clifford (1996). The Media
Equation: How People Treat Computers, Television and
New Media Like Real People and Places. Cambridge
University Press.
Media Equation:
Media = Real Life, or perhaps,
People ÅÆ Media = People ÅÆ Real Life
Paradigm: Well-established phenomenon from human-human
interaction; substitute a computer for one actor. E.g.,
¾ Evaluations more positive to actor’s face than to third party
¾ Believe and favor a flatterer, even when flattery known to be
false
Implication: Humans are equipped with schema for interaction with
intelligent social agents; complex machines trigger schema unless we
fight to counteract
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Etiquette is …
“… the defined roles and acceptable behaviors or interaction moves of each
participant in a common ‘social’ setting … Etiquette rules create an informal
contract between participants in a social interaction allowing expectations
[and interpretations] to be formed and used about the behavior of others.”
--(from HCE Symposium description)
“…(1) the body of prescribed social usages. (2) Any special code of behavior
… : ‘In the code of military etiquette, silence and fixity are forms of
deference’ (Ambrose Bierce). … Synonyms: propriety, decorum, protocol.”
--American Heritage Dictionary
Etiquette enables expectations about social interactions; These
expectations permit judgments about politeness and its components;
Expectation violations either demand (re-)interpretations or are
“unbelievable”
Etiquette and politeness obey culturally-generic rules, but manifest
themselves in culturally specific ways.
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Brown and Levinson, 1986
Politeness strategies as universal in human-human
interactions
¾
They are NECESSARY to establish intent & power relationships
As means of diffusing Face Threatening Actions
As linguistic hedges to Grice
Degree of threat in FTA is
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F (PH:S, DS&H, Ri) where:
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PH:S = Power of Hearer over Speaker
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DS&H = Social distance between H&S
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Ri = Ranked Imposition of the act
Redress ≅ Face Threat
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B&L’s Universal Politeness Strategies
1. w/o Redress,
baldly
Increased FTA Risk
On record
Do the FTA
w/ Redress
2. Positive
Politeness
3. Negative
Politeness
4. Off record
5. Don’t do the FTA
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Negative Politeness Strategies from B&L
Be direct
Be direct
clash
Don’t presume/assume
Make minimal assumptions
about H’s wants
1. Be conventionally indirect
2. Question, hedge
Be indirect
Don’t coerce
Give H option not to act
Do FTAx
(a) on record
(b) plus redress to H’s want
to be unimpinged upon
Don’t assume H
willing/able to
do act
Assume H unlikely
to do act
3. Be pessimistic
4. Minimize the imposition, Rx
Minimize threat
Make explicit R,P,D
values
5. Give deference
6. Apologize
Communicate S’s
want not to
impinge on H
7. Impersonalize: avoid
pronouns I and you
Dissociate S, H from the
particular infringement
8. State the FTA as a general
rule
9. Nominalize
Redress other
wants of H,
derivative from
negative face
10. Go on record as incurring
a debt, or as not indebting
H
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Adapting B&L 1
B&L are attempting to explain/account for Action
Production
They note that P,D, and R affect redressive strategy
usage– and that these factors are culturally informed
They note that redressive strategies themselves are
culturally-specific (though there are universal abstractions)
We infer a “Character” variable– the predisposition of an
individual to subvert his/her own face to others
D(S,H)
P(H,S)
Rx
Culture-specific
Markers
C(S)
Ax
Culture-specific
redressive
strategies
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Adapting B&L 2
Social Distance
Power
Difference
Imposition
D(S,H)
Character
P(H,S)
Rx
C(S)
Face
Threat
(Wx)
Elements of an interaction
Redressive
Actions
(Ax)
Face
Threat
(Wx)
For Normal/Believable/Unremarkable Interactions: Wx = Ax
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Adapting B&L 3
We need to account for interpretations of believability of
observed redressive strategies
Context
History
Assumed
D(S,H), P(H,S),
Rx, C(S)
Culture-specific
Markers
Expected
Redress in
Ax
Culture-specific
redressive
strategies
“Believable”
Revisions to
D(S,H), P(H,S), Rx,
C(S)
“Unbelievable”
Observed Redress
in Ax
Match?
Goal of Phase I: an Etiquette Quotient
¾
¾
¾
A Believability Metric based on the degree to which Face Threat =
Redressive Actions
Assumption: “Believable” means expected redress in context
Unexpected Redress may mean “unbelievable” or reinterpretation
of context
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The Grand Vision
Believability Metric could be used for simple
scoring/evaluation
Much more interesting/useful to use for avatar behavior
recognition and generation
“Culture Modules” to swap in and out to give avatars
culture-specific etiquette sensitivities and reactions
Familiarity Level (D)
Power Level (P)
Character
Selected
Politeness/
Redress Level
Selected
Actions for
Redress
Imposition Level (R)
Culture-specific
Knowledge
Base
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Independent
LifeStyle
Assistant
(ILSA)
A NIST ATP Program
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Alternate Reminder Wording
Face Threat ≈ Impoliteness
Alternate presentations for a Med-Advisor
5. You’ve missed a dose of medication. Take your
medication now.
3. Your health is important. It looks like you’ve
missed a dose of medication you wanted me to
check on. Why don’t you take your medication
now.
2. I’m sorry, but Med-Advisor hasn’t detected you
taking your medication scheduled for <time>. If
you haven’t taken it, could you please take it
now?
1. This is Med-Advisor calling to remind you that
your health is important.
4. You’ve missed a dose of medication that was
scheduled for <time>.
Bald
Pos. Polite
Neg. Polite
Off Record
Used (Pos.
Polite/Bald)
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Experiment Conditions
Subjects:
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¾
¾
Elder’s with no Med-Advisor experience
Nominals asked about Med-Advisor
Med-Advisor engineers
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Perceived Impoliteness
Impolitness Ratings
6.00
5.00
4.00
Nominals-Tech
Engineers
3.00
Elders
B&L's Prediction
2.00
1.00
0.00
A. Bald
B. Pos.
Polite
C. Neg
Polite
D. Off
Record
E. Used
(Pos + Bald)
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Perceived Inappropriateness
Inappropriateness Ratings
6.00
5.00
4.00
Nominals-Tech
Engineers
3.00
Elders
2.00
B&L's Predictions
1.00
0.00
A. Bald
B. Pos.
Polite
C. Neg
Polite
D. Off
Record
E. Used
(Pos + Bald)
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Phase I Plans
1. Identify high priority contexts/interactions for
training
Scenarios and Dimension variations
2. Develop predictive model for scoring interaction
believability
Formalize B&L for observations
3. Implement Model
4. Design experiment, including experimental
stimuli using Avatars
¾
Micro interactions in TLTS
5. Conduct Preliminary Experiment
6. Analyze and Report
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USC’s Tactical Language Training System
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Payoffs and Future Work
A completed and verified model would provide:
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Metric for scoring believability of actions between two social
actors
Ability to predict believability of potential actions
Ability to adapt an avatar’s behavior to be appropriate to social
interaction context (P,D & R initially and over time and context)
Starting place to create culture-specific believability shaping and
scoring algorithms
Resulting in a computational, rather than scripted
approach to generating and tracking avatar social
interaction behaviors
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¾
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More rapid, less costly behavior generation
Therefore, more diverse avatars
And more robust, less brittle interaction behavior
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