Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology...

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Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology Research Institute Aerospace, Transportation, and Advanced System Lab Atlanta USA

Transcript of Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology...

Page 1: Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology Research Institute Aerospace, Transportation, and Advanced.

Emergent Social Phenomena and Social Action Selection

Alan R Wagner

Georgia Institute of Technology Research Institute

Aerospace, Transportation, and Advanced System Lab

Atlanta USA

Page 2: Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology Research Institute Aerospace, Transportation, and Advanced.

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What does the I in HRI stand for?

Interaction: influence—verbal, physical or emotional—by one individual on another (Sears, Peplau, & Taylor, 1991).

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How can we represent interaction computationally?

Game TheoryStrategic interactionAssumes that each

player maximizes influence

Interesting equilibrium results

Normal form game

Interdependence Theory

Constructed from psychological experiments

No assumptions about player’s motives

No equilibrium resultsOutcome matrix

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The Outcome Matrix/Normal Form Game

Representation: A finite set of N

individuals For each iN a

nonempty set of actions Ai

A scalar outcome value (o) for every pair of actions (influence)

-i used to represent the robot’s partner

Outcome Matrix

Person 1

Per

son

2

Accept 5 5 8 2

Reject

0 0 0 0

Fair Unfair

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Social Situations and Interaction

8 8 0 12

12 0 1 1

An interaction is a discrete period of influence between two individuals choosing between particular actions

Social situation is an abstract representation of a class of interactions involving particular outcome values

8 8 0 12

12 0 1 1

Tom

Eri

cAdmit Guilt

Claim innoc.

Admit GuiltClaim innoc.

ai1 ai2

a-i1

a-i2

-i

i

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The Space of Social Situations

Interdependence space (Kelley, et al. 2003)

Location in the space provides info. about the situation

The dimensions are: Interdependence Correspondence Control Symmetry – not shown

Inte

rdep

ende

nce

A

xis

Inde

pend

ent

D

epen

dent

Correspondence of Outcomes Axis Basis of Control

Axis

Coordination

Exchange

3-D Interdependence Space

PD Game

HERO

CHICKEN

TRUST

Colored parallelograms denote social situations

Corresponding

Conflicting

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Deception (joint with Ron Arkin)

Deception is commonly used by animals

The use of deception by primates may indicate Theory of Mind (Byrne & Whiten, 1990) Signal exaggeration, signal

suppression, distractions, hiding objects, etc.

May be a hallmark of social intelligence (Byrne & Whiten, 1990)

Mimicry

Camouflage

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Defining Deception

Deception: “Causing another to believe what is not true; to mislead or ensnare.” (Webster’s, 1999)

Deliberately induced misperception (McClesky, 1991)

A false communication that tends to benefit the communicator (Bond and Robinson, 1988)

We use Bond and Robinson’s definition because of its general applicability

Page 9: Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology Research Institute Aerospace, Transportation, and Advanced.

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Situations Warranting Deception

Deception is a false communication that tends to benefit the communicator (Bond and Robinson, 1988)

1) Deceiver provides false communication. This implies conflict.

2) Deceiver benefits from communication. This implies dependence.

Correspondence Dimension

Conflicting Outcomes

Corresponding Outcomes

Interdependence Dimension

Dependent Outcomes

Independent Outcomes

Area maximally warranting deception

Situations warranting deception • k1

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Computationally Representing Deception

Mark

Dec

eive

r Play dead x 9 x 2

Don’t Play Dead

x x x x

Approach Stay away

Deceiver and Mark choose actions Deceiver selects actions that will influences

Mark’s behavior Play dead so that Mark will approach

Induced Matrix

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Computationally Representing Deception

Mark

Dec

eive

r Play dead x 9 x 2

Don’t Play Dead

x x x x

Approach Stay away

Deceiver’s actions make Mark think that one action is more rewarding or less costly then it actually is

-3

True value

True Matrix

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Trust

Idea: Some social situations require trust and others do not

Can we use our representation of social situations and interaction to classify situations in terms of trust?

Page 13: Emergent Social Phenomena and Social Action Selection Alan R Wagner Georgia Institute of Technology Research Institute Aerospace, Transportation, and Advanced.

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Defining trust

Trust is a belief, held by the trustor, that the trustee will act in a manner that mitigates the trustor’s risk in a situation in which the trustee has put its outcomes at risk.

Key point: risk

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Classifying situational trust

Trustor

Tru

stee

Action ALean back

Action BDon’t lean

back

A trustor is the individual doing the trusting. The trustee is the individual being trusted

Let action A be the trusting action and action B be the untrusting action

Action C Catch

Action DDon’t catch

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Classifying situations in terms of trust

Trustor

Tru

stee

If the trustor doesn’t trust the trustee, then their outcome is 6, and the trustee’s action doesn’t matter, i.e. no risk!

6 12 4 6

6 0 4 6

If the trustor trusts the trustee, then they risk 6 for a possible gain of 12

If the trusting action is selected, then the trustee’s choice of action determines the trustor’s outcome

Action C Catch

Action DDon’t catch

Action ALean back

Action BDon’t lean

back

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Conditions for Situational Trust

Trustor

Tru

stee

We can thus list some conditions for trust:1. It is better for trustor if the trustee acts in a

trusting manner (12 > 0 )2. It is better for trustor to select the untrusting

action, than for the trustor to trust and have the trustee not act in a trusting manner (from example 12 > 6 > 6 > 0 )

6 12 4 6

6 0 4 6Action C

Catch

Action DDon’t catch

Action ALean back

Action BDon’t lean

back

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How much Trust?

Trust risk Risk: expectation over a loss function L

R(x) = L(x,y)p(x) where x is predicted y is the true value

Partner Model related

Context/Situation related loss

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Thank you

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References

Carpin, S., Jijun Wang, Michael Lewis, Andreas Birk, Adam Jacoff: High Fidelity Tools for Rescue Robotics: Results and Perspectives. RoboCup 2005: 301-31.

Deutsch, M. (1962). Cooperation and trust: Some theoretical notes. In M. R. Jones (Ed.), Nebraska symposium on motivation (pp. 275-315). Lincoln, NB: University of Nebraska.

Gambetta, D. (1990). Can we trust trust? In D. Gambetta (Ed.), Trust, making and breaking cooperative relationships (pp. pages 213--237). Oxford England: Basil Blackwell.

Kelley, H. H., J. G. Holmes, N. L. Kerr, H. T. Reis, C. E. Rusbult, and P. A. M. V. Lange, An Atlas of Interpersonal Situations. Cambridge University Press, New York, NY, 2003.

H. H. Kelley and J. W. Thibaut, Interpersonal Relations: A Theory of Interdependence. John Wiley & Sons, New York, NY, 1978.

Luhmann, N. (1979). Trust and power.Chichester: Wiley Publishers. Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. Cambridge, MA: MIT Press. Relationship.

(2000). In American heritage dictionary. Rusbult, C. E., & Van Lange, P. A. M. (2003). Interdependence, interaction and relationship. Annual Review of

Psychology, 54, 351-375. Sears, D. O., Peplau, L. A., & Taylor, S. E. (1991). Social psychology.Englewood Cliffs, New Jersey: Prentice

Hall.