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Comme il Faut: Enabling Playable Social Models
Josh McCoy, Mike Treanor, Ben Samuel, Michael Mateas, NoahWardrip-Fruin
University of California Santa Cruz, Expressive Intelligence Studio
1156 High Street
Santa Cruz, CA 95064
{mccoyjo, mtreanor, bsamuel, micahelm, nwf}@soe.ucsc.edu
Abstract
Performing and responding to patterns of social interaction
while remaining within the bounds of social norms defined
by cultural context is a task most humans perform many
times each day. Though taken for granted by people, social
interaction is difficult to implement in artificial intelligence
(AI) systems. The social AI system Comme il Faut (CiF)
aims to take a step forward in this area by allowing agents to
interact socially with one another. CiF is a playable model
of social interaction constructed by using concepts from the
social sciences as both the basis for algorithms and as a
framework of representation. When used to encode and
process human domain knowledge of social behavior, this
system models the "social physics" that govern social
interaction. In this paper, the knowledge representations and
the algorithms used by CiF are detailed with examples of
how CiF is currently being used in simulations, computer
games, and interactive media experiences.
Introduction
Performing and responding to patterns of social interaction
while remaining within the bounds of social norms defined
by cultural context is a task most humans perform many
times each day. However, social interaction is difficult to
map to artificial intelligence systems for a variety of
reasons. Representing social commonsense to help build
context around interactions; engineering re-usable patterns
of social behavior that are general enough to be used in a
variety of context but specific enough to convey the
changes in the social environment; and determining what
changes to the social state are appropriate given the current
social context and the history of the specific agent in the
social interaction are all representative of the challenges
facing AI systems whose goal is to perform humanunderstandable social reasoning and interaction.
Copyright © 2011, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
As daunting as these obstacles in the face of building
such an AI system may be, we can begin to overcome them
by leveraging the analysis done in social science and the
humanities to build a methods of representing and using
those methods of representation to encode the knowledge
of domain experts (of which most people are)
Comme il Faut (CiF) is an AI system that uses these
techniques to enable an interactive, authorable model of
social interaction for autonomous agents. {SGs are the core
of the KR in CiF}{CiF is used by The Prom, Holodeck
project, and the Siren project – using it in many projects
with social interaction is a form of validation for CiF.}
Related Work
{related systems that perform social interaction in an AI
system} {what about general positioning in KR literature?}
Knowledge Representation in CiF
{high-level intro to social games}
Rules
{Rules and the predicates they are composed of.}
Influence Rule Sets
{description of influence rule sets.}
Microtheories
{description of Microtheories}
Social Games
{more detail explanation of social games.}
Examples of Representation
The following example will illustrate the structures
described above, and will be used again to demonstrate the
algorithms. The example is form The Prom, CiF's
inaugural application, which is set in the domain of a
dramatic high school.
Simon is a character with the traits of being a weakling
and witty. Naomi is another character with the trait of
attractive. Simon has the status of having a crush on
Naomi, and Naomi is has the status of popular. Naomi and
Simon have the relationship of being friends. Simon has
high romance network values toward Naomi and she has
very low romance scores toward him. Naomi also has low
cool network values toward Simon. All other network
values are neutral. The cultural knowledge base states that
Simon likes objects labeled as "lame," such as scientific
calculators, and Naomi likes things that are "cool," such as
football. In the social fact database is an entry marked as
something embarrassing Simon has done toward Naomi. It
is described as "Simon misunderstood Naomi asking for
help on homework as a romantic advance."
This above situation describes how CiF represents the
unfortunate situation where a nerdy character has
unrealistic hopes of having a relationship with someone
"out of his league."
Algorithms in CiF
{overview of what algorithms are in CiF and what they
operate over (the previous KR)}
Desire Formation
The following example will demonstrate the details of why
Simon wants to perform an action that would raise Naomi's
sense of romance for him (the romance network) and why
Naomi rejects him so hard.
Simon and Naomi have the relationship of friends and
Simon status of having a crush on Naomi. These two facts
brings the influence rules from the microtheories for
relationship(friends,x,y) and status(has a crush on,x,y). The
friends microtheory contains an influence rule that would
detract from a character's desire to be romantic with the
person he or she is friends with. However, the microtheory
for having a crush on someone contains an influence rule
with a positive weight greater than the negative weight of
the friend microtheory's rule. Furthermore, additional
microtheories for Simon's high romance network values
toward
Naomi
(romanceNetwork(greater
than
66,Simon,Naomi)) would contribute to this desire. When
the weights from all true rules across all microtheories are
summed, the net result is that Simon wants to perform a
social game that has the intent to raise Naomi's romantic
feelings
toward
him
(intent(Simon,romanceNetwork(+,Naomi,Simon))).
Though there could be many social games defined with
this intent, the two considered here will be a physical flirt
and a conversational flirt. These two social games are
similar by intent, but different because their preconditions,
influence rule sets and effects differ drastically. Simon's
desire to play one over the other will be determined by the
initiator influence rule sets in a process very similar to the
microtheory's. For example, because Simon has the trait of
weakling (trait(Simon,weaking)), and there is a rule with
negative weight pertaining to that trait in physical flirt, he
would be less likely to want to play that game.
Furthermore, conversational flirt has an influence rule for
witty (trait(Simon,witty)), another of Simon's traits, that
makes him more likely to play that social game. For each
social game, the net value of the weights of the true
initiator influence rules add to the value of all true initiator
influence rules from the microtheories to form the total
desire to play each game.
FormDesire(I, r, o, S, M):Pi
Pi = {}
For each s in S
if s does not require an other
if eval( sprecondition, i, r)
v = 0;
for each m in M
if eval(mdefinition, i, r)
for each influenceRule in minfluenceRuleSet
if eval(influenceRule, i, r)
v += influenceRuleweight
for each influenceRule in sinitiatorInfluenceRuleSet
if eval(influenceRule, r, i)
v += influenceRuleweight
push <s, i, r, v> on to Pi
if s requires an other
for each o in cast where o ≠ i and o ≠ r
if eval( sprecondition, i, r, o)
v = 0;
for each m in M
if eval(mdefinition, i, r, o)
for each influenceRule in minfluenceRuleSet
if eval(influenceRule, i, r, o)
v += influenceRuleweight
for each influenceRule in sinitiatorInfluenceRuleSet
if eval(influenceRule, r, i, o)
v += influenceRuleweight
push <s, i, r, o, v> on to Pi
return Pi
Intent Selection
Once Simon's desires are formed, the intent selection
process determines exactly what he chooses to do. Intent
selection is a natural place to allow for interaction. In the
case of The Prom, the players choose among the top five
desires.
Social Game Play
Assuming the intent selection process resulted in having
Simon play conversational flirt with Naomi, the next step
is to determine how Naomi will respond. The same
relationship(friend,x,y) microtheory rule that detracted
from Simon's desire to play a game that would increase
Naomi's romance network toward Simon, also affect's the
responder's desire to accept or reject the intent of the game.
Additionally, another microtheory is brought into play by
the fact that Naomi is friends with Cassie who has a crush
on Simon (relationship(friends,x,z) ^ status(has a crush
on,z,y)). In this microtheory is a rule that detracts from a
character's desire to play or accept a game that would
increase romance with the character that a friend has a
crush on. Even worse for Simon, Naomi has the status of
popular and Simon does not (status(x,popular) ^
~status(y,popular)). This also will detract from Naomi's
desire to accept the intent of the game.
The previous influence rules pertained to Naomi's desire
to accept or reject Simon's intention to raise her feelings of
romance toward him in general. The responder influence
rule set and the effect conditions will determine exactly
how she responds. For example, conversational flirt has a
negatively weighted responder influence rule for if Simon
likes something that is labeled as lame that Naomi doesn't
like
from
the
cultural
knowledge
base
(ckb(x,likes,y,dislikes,lame)). By our earlier example, we
know that Simon likes scientific calculators and Naomi
doesn't.
By the microtheory and responder influence rules, it is
determined that Naomi will reject Simon's advances. But
the way in which she rejects him is determined by the most
salient effect condition. In this case, is an effect condition
that matches the responder rule about the cultural
knowledge base. The effect social change tied to that effect
condition is that Naomi's cool network decrease by 20 and
the interaction is labeled in the social fact database as
something embarrassing that Simon did to Naomi
(coolNetwork(-20,Naomi,Simon)
^
SFDBLabel(embarrassing,Simon,Naomi)). In The Prom,
effects are tied to comic book like performance
instantiations where characters engage in authored
dialogue pertaining to the effect conditions.
PlaySocialGame(i, r, o, S, M):effect
vr = 0, accept = false, validEffects={}, outcome={}
for each m in M
if eval(mdefinition, i, r, o)
for each influenceRule in minfluenceRuleSet
if eval(influenceRule, r, i, o)
vr+= influenceRuleweight
for each influenceRule in sresponderInfluenceRuleSet
if eval(influenceRule, r, i, o)
vr+= influenceRuleweight
if vr > 0
accept = true
for each effect in seffects
if eval(effectcondition, i, r, o, accept)
push e on to validEffects
outcome = mostSalient(validEffects)
valuate(outcomesocialChange)
return outcome;
Triggers
After the social game has finished being played and the
effect changes have taken effect, the all trigger rules are
checked against the state. In this case, because there are
more than two entries in the past entries in the social fact
database, Simon is given the status of embarrassed.
At this point, all characters form their desires again.
Note that the above example was extremely simplified.
With the current set of microtheories and social games,
together containing over unique 3,500 influence rules, each
social game has over twenty rules that contribute to their
desire and responses.
Conclusions
{What we learned}
{Rule tuning is hard – tool helps}
{Taking social science as a foundation for an AI system is
not a straightforward task.}
References