Influence of anthropomorphic agent on human empathy through games

The social acceptance of AI agents, including intelligent virtual agents and physical robots, is becoming more important for the integration of AI into human society. Although the agents used in human society share various tasks with humans, their cooperation may frequently reduce the task performance. One way to improve the relationship between humans and AI agents is to have humans empathize with the agents. By empathizing, humans feel positively and kindly toward agents, which makes it easier to accept them. In this study, we focus on tasks in which humans and agents have various interactions together, and we investigate the properties of agents that significantly influence human empathy toward the agents. To investigate the effects of task content, difficulty, task completion, and an agent's expression on human empathy, two experiments were conducted. The results of the two experiments showed that human empathy toward the agent was difficult to maintain with only task factors, and that the agent's expression was able to maintain human empathy. In addition, a higher task difficulty reduced the decrease in human empathy, regardless of task content. These results demonstrate that an AI agent's properties play an important role in helping humans accept them.Comment: 17 pages, 12 figures, 5 tables, submitted IEEE Access. arXiv admin note: substantial text overlap with arXiv:2206.0612

Extending Commands Embedded in Actions for Human-Robot Cooperative Tasks

ArticleInternational Journal of Social Robotics. 2(2):159-173 (2010)journal articl

Modeling Trust and Reliance with Wait Time in a Human-Robot Interaction

This study investigated how wait time influences trust in and reliance on a robot. Experiment 1 was conducted as an online experiment manipulating the wait time for the task partner's action from 1 to 20 seconds and the anthropomorphism of the partner. As a result, the anthropomorphism influenced trust in the partner and did not influence reliance on the partner. However, the wait time negatively influenced trust in and reliance on the partner. Moreover, a mediation effect of trust from the wait time on reliance on the partner was confirmed. Experiment 2 was conducted to confirm the effects of wait time on trust and reliance in a human-robot face-to-face situation. As a result, the same effects of wait time found in Experiment 1 were confirmed. This study revealed that wait time is a strong and controllable factor that influences trust in and reliance on a robot

Improving of Robotic Virtual Agent's errors that are accepted by reaction and human's preference

One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. In this study, we focused on a task between an agent and a human in which the agent makes a mistake. To investigate significant factors for designing a robotic agent that can promote humans empathy, we experimentally examined the hypothesis that agent reaction and human's preference affect human empathy and acceptance of the agent's mistakes. The experiment consisted of a four-condition, three-factor mixed design with agent reaction, selected agent's body color for human's preference, and pre- and post-task as factors. The results showed that agent reaction and human's preference did not affect empathy toward the agent but did allow the agent to make mistakes. It was also shown that empathy for the agent decreased when the agent made a mistake on the task. The results of this study provide a way to control impressions of the robotic virtual agent's behaviors, which are increasingly used in society.Comment: 13 pages, 4 figures, 5 tables, submitted ICSR2023. arXiv admin note: text overlap with arXiv:2206.0612

Facilitation of human empathy through self-disclosure of anthropomorphic agents

As AI technologies progress, social acceptance of AI agents including intelligent virtual agents and robots is getting to be even more important for more applications of AI in human society. One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. By empathizing, humans take positive and kind actions toward agents, and emphasizing makes it easier for humans to accept agents. In this study, we focused on self-disclosure from agents to humans in order to realize anthropomorphic agents that elicit empathy from humans. Then, we experimentally investigated the possibility that an agent's self-disclosure facilitates human empathy. We formulate hypotheses and experimentally analyze and discuss the conditions in which humans have more empathy for agents. This experiment was conducted with a three-way mixed plan, and the factors were the agents' appearance (human, robot), self-disclosure (high-relevance self-disclosure, low-relevance self-disclosure, no self-disclosure), and empathy before and after a video stimulus. An analysis of variance was performed using data from 576 participants. As a result, we found that the appearance factor did not have a main effect, and self-disclosure, which is highly relevant to the scenario used, facilitated more human empathy with statistically significant difference. We also found that no self-disclosure suppressed empathy. These results support our hypotheses.Comment: 20 pages, 8 figures, 2 tables, submitted to PLOS ONE Journa