A Socio-cognitive Approach to Personality: Machine-learned Game Strategies as Cues of Regulatory Focus

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 machine-learned 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.