Training the use of theory of mind using artificial agents

When engaging in social interaction, people rely on their ability to reason about unobservable mental content of others, which includes goals, intentions, and beliefs. This so-called theory of mind ability allows them to more easily understand, predict, and influence the behavior of others. People even use their theory of mind to reason about the theory of mind of others, which allows them to understand sentences like Alice believes that Bob does not know about the surprise party'. But while the use of higher orders of theory of mind is apparent in many social interactions, empirical evidence so far suggests that people do not use this ability spontaneously when playing strategic games, even when doing so would be highly beneficial. In this paper, we attempt to encourage participants to engage in higher-order theory of mind reasoning by letting them play a game against computational agents. Since previous research suggests that competitive games may encourage the use of theory of mind, we investigate a particular competitive game, the Mod game, which can be seen as a much larger variant of the well-known rock-paper-scissors game. By using a combination of computational agents and Bayesian model selection, we simultaneously determine to what extent people make use of higher-order theory of mind reasoning, as well as to what extent computational agents can encourage the use of higher-order theory of mind in their human opponents. Our results show that participants who play the Mod game against computational theory of mind agents adjust their level of theory of mind reasoning to that of their computer opponent. Earlier experiments with other strategic games show that participants only engage in low orders of theory of mind reasoning. Surprisingly, we find that participants who knowingly play against second- and third-order theory of mind agents apply up to fourth-order theory of mind themselves, and achieve higher scores as a result

Design of an agent-based learning environment for high-risk doorstep scam victims

Item does not contain fulltextDoorstep scams are scams, often happening at the front door, in which a con artist has a convincing, but fraudulent, story with the purpose of coming into your house and/or stealing money. Various campaigns to educate people exist, but they do not focus on the verbal skills people can use to prevent themselves from becoming a victim. This paper describes the conceptual design of a proposed training application. This application will provide an agent-based learning environment for high-risk doorstep scam victims. In order to create a training application, field research has been done to the content and progress of doorstep scams, which is used to create interactive scenarios.International Workshops of PAAMS 2018 (Toledo, Spain, June 20-22, 2018

Design of an agent-based learning environment for high-risk doorstep scam victims

Doorstep scams are scams, often happening at the front door, in which a con artist has a convincing, but fraudulent, story with the purpose of coming into your house and/or stealing money. Various campaigns to educate people exist, but they do not focus on the verbal skills people can use to prevent themselves from becoming a victim. This paper describes the conceptual design of a proposed training application. This application will provide an agent-based learning environment for high-risk doorstep scam victims. In order to create a training application, field research has been done to the content and progress of doorstep scams, which is used to create interactive scenarios