Matching artificial agents' and users' personalities: designing agents with regulatory-focus and testing the regulatory fit effect

Artificial agents are becoming more than human-computer interfaces: they are becoming artificial companions, interacting on a long-term basis and building a relationship with the user. This evolution brought new challenges, such as designing agents with personalities to the benefits of users. We endow artificial agents with regulatory focus, taking a sociocognitive approach of personality, by using machine-learning techniques. We test whether this personality can be perceived by users and if there is a regulatory fit effect on the users credibility judgement of the agent (i.e. is the agent perceived as more credible if its regulatory focus is the same as that of the user?). Our results show agents regulatory focus can be adequatly perceived by users playing a board game against an agent expressing its regulatory focus via machine-learned strategies. A regulatory fit effect was found on the likeability judgment for prevention focus users but not for promotion focus users.