A Robot’s Experience of Another Robot: Simulation

Tibor Bosse

Johan F. Hoorn

Matthijs Pontier

Ghazanfar F. Siddiqui

Overview of this presentation

Introduction to I-PEFiC ModelImplementationResults simulation experimentsDiscussionFuture steps

Goal of this study

Let virtual agent / robot perceive other agent, or human By determining its engagement with its interaction partner, a virtual

agent can show an appropriate affective response

Formalise well-validated I-PEFiC model about how humans perceive virtual characters, and use it to let virtual agents perceive each other, or their users

Interesting for any application which requires human-like emotional behaviour / communication; e.g.:

(serious) games, e-therapy, agents that serve as practice subjects for therapists or police officers, etc.

Variables in I-PEFiC model

Ethics Is agent to meet good or bad? Aesthetics Is agent to meet beautiful or ugly? Epistemics Is agent to meet realistic or unrealistic? Affordances Is agent aid or obstacle (regarding goals) Similarity Is agent like me? Relevance Is agent relevant for me? (regarding goals) Valence Is expected outcome of interaction with agent

good or bad? Involvement Subjective tendency to approach Distance Subjective tendency to avoid

Variables are unipolar

Introduction to I-PEFiC model

Domain

Soccer agents you might remember from earlier presentations

Virtual soccer fans communicate with each other in a virtual world

They can decide to meet with each other and talk about certain conversation subjects

Implementation

Agents perceive each others features good, bad, realistic, and unrealistic using a designed value [0, 1] (what would the average agent think) and a (personal) bias [0, 2]; e.g.,

Perceived(Beautiful, A1, A2) = Bias(A1, A2, Beautiful) * Designed(Beautiful, A2)

Implementation Ethics

A1 perceives ethics A2 according to club clothes A2 is wearing; e.g.,

Perceived(Good, A1, A2) = Satisfaction(A1, Club A2)

Teammates are good guys

Supporters of rival teams are bad guys

Implementation Affordances

A1 perceives affordances A2 according to expected communication possibilities, based on presuppositions about language skills (in Dutch, English and Urdu) based on outer appearance; e.g.,

Perceived(Aid, A1, A2) =

(ExpectedSkill(A1, A2, language) * Skill(A1, language))

Implementation Similarity

Agents perceive their own features: Perceived(Feature, A1, A1) = Bias(A1, A1, Feature) * Designed(Feature, A1)

Agents compare this self-perception with perception other to determine similarity:

Similarity(A1, A2) =

1- ((simfeature * abs(Perceived(Feature, A1, A2) – Perceived(Feature, A1, A1) ))

simfeature is the (regression) weight of each feature for similarity

Relevance, Valence and Engagement

All formulas have the form: A = B*B + C*C + D*D + CD*C*D

B = (regression) weight main effect B on A

CD = weight interaction effect C and D on A

Relevance, Valence and Engagement

Effects on: Main effects Interaction effects

Relevance EthicsAestheticsEpistemicsAffordances

Ethics x AffordancesEthics x Aesthetics x Epistemics

Valence EthicsAestheticsEpistemicsAffordances

Ethics x AffordancesEthics x Aesthetics x Epistemics

Engagement SimilarityRelevanceValence

Relevance x Valence

Conclusions from experiments

Higher values for attributes will result in higher Involvement / Distance

Involvement / Distance from A1 to A2: Inv. Dist. A2 is an aid for A1 (speaks same language) ++ -- A2 is an obstacle for A1 (speaks different language) -- ++

A2 is a good guy (wears same club clothes) ++ + A2 is a bad guy (wears rival club clothes) + ++

A2 is beautiful ++ + A2 is ugly + ++

A2 is realistic ++ + A2 is unrealistic + ++

Conclusions from experiments

The relative differences between realistic / unrealistic are smaller than those between beautiful / ugly

Realistic / unrealistic have less influence than beautiful / ugly

Discussion

Model is adequate for simulating engagement Positive features do not exclusively increase level of

involvement, and negative features do not exclusively increase level of distance, which corresponds to empirical evidence

Future Steps

Combine I-PEFiC (emotion elicitation) with Gross (emotion regulation), so virtual agents can perform affective response selection

Validate model against empirical data human affective trade-off processes

Questions?