A Socio-cognitive Approach to Personality: Machine-learned Game Strategies as Cues of Regulatory Focus
Résumé
Artificial agents are becoming artificial companions,
interacting with the user on a long-term basis. This evolution
brought new challenges to the affective computing domain, such
as designing artificial agents with personalities to the benefits of
the user. Endowing artificial agents with personality could help
to increase the agent’s believability, hence easing the interaction.
This paper touches on two questions pertaining to computational
personality modeling: 1/ how to produce artificial personalities
which can inform personality researchers, whether from computer sciences or psychology and 2/ will behaviors produced by
artificial agents be perceived by users as putting the programmed
personality across as such. We propose to use a data-driven
approach to endow artificial agents with personality, using the
regulatory focus theory as a framework. We used machinelearned game strategies, in the form of alternative decision
trees computed from human data, to convey the personality of
artificial agents. We then tested whether these personalities can
be perceived by users after playing a game against these agents.
We used two artificial agents as controls: one randomly playing
and one with an ”average / depersonalized” strategy. On the
one hand, our results show that agents’ regulatory focus, when
programmed, can be accurately perceived by users. On the other
hand, our results also point out that personality will be perceived
by users even if the agent’s design does not intend to transmit
one