In this study, we attempted to improve the engagement in tasks involving interactions with an agent by making the agent aware of the temporal continuity of information shared through interactions between the user and agent. We conducted an experiment to test the effectiveness of the agent behavior model using a game task. During the experiment, we investigated the degree of engagement of the participants with the task, the workload of the task during the game, the mental load of the task, and the sense of immersion in the task. As a result, it was found that the amount of work in the task and the degree of engagement with the task of the participant could be improved, as could the influence of the agent’s action. These results suggest that demonstrating the retention of time-continuous user information in human-agent interactions is effective in improving the engagement with a task.

In the field of Human-Agent Interaction (HAI), continuation of interaction is one of the main areas of research. If the behavior of the agent is too predictable, humans stop interacting with it when they get bored. In this study, we aim to build agents that people want to keep interacting with, by employing the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. As a preliminary step, we attempt to clarify the conditions required to maintain interaction between humans and agents, by modeling the experiences of fun and boredom based on intrinsic motivation.

We aimed to investigate whether the relationship between the behavior of humans and that of the surrounding agents improves the virtual experience. We conducted an experiment using crowd agents that implement the behavior propagation model based on the herd behavior model. The result indicated that the experiences in the virtual world improved to some extent.

An interactive decision-making is useful to put our ambiguous desires into concrete through the interaction with others. However, in human-agent interaction, the agents are often not regarded as well-experienced consultants but rather as human-centered interfaces that provide information. We aimed to induce an active human attitude toward decision-making interactions with agents by controlling the speech reaction time (SRT) of the agents in order to consider the agents as reliable consultants. We conducted an experiment to investigate whether the SRT could influence the human participant's attitude. We used two kinds of agents; one had no SRT (no-SRT) and the other had a SRT of two seconds (2s-SRT). As a result, we found that the no-SRT agents could keep the participants' speech reaction times short even during the decision-making task in which the participants need time for careful consideration. In addition, from the analysis of the number of proposed categories and participant's behavior, we suggest that the participants had an active attitude toward interaction with no-SRT agents.

Share on Controlling the Timing of Metacognitive Suggestions using Impasse Estimation Authors: Yoshimasa Ohmoto Kyoto University, Kyoto, Japan Kyoto University, Kyoto, JapanView Profile , Hanako Sonobe Kyoto University, Kyoto, Japan Kyoto University, Kyoto, JapanView Profile , Toyoaki Nishida Kyoto University, Kyoto, Japan Kyoto University, Kyoto, JapanView Profile Authors Info & Claims HAI '18: Proceedings of the 6th International Conference on Human-Agent InteractionDecember 2018 Pages 353–355https://doi.org/10.1145/3284432.3287184Published:04 December 2018Publication History 0citation33DownloadsMetricsTotal Citations0Total Downloads33Last 12 Months4Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In human-computer interaction, it is first necessary for the human to recognize that there is a meaning beyond information provision while interacting with an agent. One of the critical barriers to social human-computer interaction is the mental state of people with respect to agents. Intentional stance is defined to be an appropriate mental stance in human-agent interaction. To induce intentional stance, we showed interactions between multiple agents in this study. The aim of this study is to investigate whether observing interactions between multiple agents can encourage and maintain the intentional stance toward the agents. We conducted experiments in which participants performed a series of tasks to build a house in a virtual world. In this task, two agents helped and interacted with the participants. The participants were divided into two groups; a group that observed the interaction(observation-group) and a group that directly interacted with the agents(direct-group). From the data obtained in the experiment, we investigated whether the participants tried to interact with the agent that analyzed the participant's behavior and questions. As a result, we could confirm that observing the interaction induced the intentional stance, but the effect was weakened in some cases.

To establish social relationships between a human and an artificial agent, the agent has to induce and maintain the intentional stance on its human partner. In this study, we focus on contingency, which is the behavior that occurs synchronously with the last action, and the icebreaker, which is a facilitation exercise that helps start an interaction. The aim of this study is to investigate whether an agent that implements contingent responses is capable of inducing and maintaining an intentional stance. We conducted an experiment using the contingent agent and a ``subgoal-oriented agent' as a control group. As a result, we conclude that the contingent responses are capable of maintaining the intentional stance during the main task from the behavior analysis. On the other hand, we suggested that only the participants who actively joined the icebreaker with the contingent agent be induced into taking an intentional stance. From these results, we conclude that the contingent responses could maintain the intentional stance but not induce it.

Virtual agents are useful for simulations of real-world events or processes designed for solving a problem, such as serious games and gamifications. In several types of the virtual world simulations, multiple-to-multiple interaction is needed because our social interaction is multiple-to-multiple in many cases. In this study, we extended the agent behavior model that induces intentional stance in one-to-one human-agent interaction, which was proposed in our previous works, to the multiple-to-multiple human-agent interaction. By using the agent group, we investigated effects of the perspectives that influenced the human mental stance in the multiple-to-multiple human-agent interaction. To investigate the effects of the perspectives, we changed the viewpoints (subjective or objective) in different perspectives (physical or mental) when people interacted with a team of agents in the virtual futsal game. As a result, we can find that there is a different effect between the physical perspective and the mental perspective. The subjective viewpoint of the physical perspective enhanced the feeling of the agent team’s intelligence, but the objective viewpoint of the mental perspective enhanced the feeling of the agent team’s intelligence.

We examined the interaction in face-to-face selling situation. In particular, we examined how customer's nonverbal behaviors were related to their preference in a natural environment. We found that customers' body posture could be a good predictor about their preference. We also found that estimation on customer's preference could be achieved from two nonverbal behaviors better than single behavior. We discuss the present contributions toward constructing the efficient agent systems.

The virtual world simulation has many advantages. However, the effects of the experience strongly influence the mental stance of the participants. To establish social relationships between a human and an artificial agent, the agent has to induce and maintain the intentional stance in its human partner. The purpose of this study was to investigate the method to induce the intentional stance in human participants of one-to-one human-agent interaction by using the alternating estimation of local objectives and global purposes by a network-connected two-layer model. We conducted an experiment to evaluate effect of the proposed method. The results suggested that this method was successful in inducing and maintaining the intentional stance over time.

The story base interpretation is subjectively summarised and segmented from the first-person viewpoint. However, we often need to objectively represent an entire image by integrated knowledge. Yet, this is a difficult task. We proposed a novel approach, named the synthetic evidential study (SES), for understanding and augmenting collective thought processes through substantiated thought by interactive media. In this study, we investigated the kind of data that can be obtained through the SES sessions as interpretation archives and whether the database is useful to understand multiple story timelines. For the purpose, we designed a machine-readable interpretation data format and developed support systems to create and provide data that are easy to understand. We conducted an experiment using the simulation of the projection phase in SES sessions. From the results, we suggested that a “meta comment” which was deepened interpretation comment by the others in the interpretation archives to have been posted when it was necessary to consider other participants’ interpretation to broaden their horizons before posting the comment. In addition, the construction of networks to represent the relationships between the interpretation comments enabled us to suggest the important comments by using the degree centrality.

Our goal is to make a system to detect the times at which one almost laughed but he or she did not show their laughter on his/her face. We define this kind of laughter as hidden laughter. To accomplish this goal, we first tried making decision trees to detect one's amusement, the input data of which were physiological indices. We used 10-fold cross validation to evaluate the trees, and their accuracy was more than 70%. In addition, we investigated the effect of cultural background on the accuracy.

In the near future, it is expected for us to communicate with robots and computer agents in a natural way. In daily life, we usually speculate about partner's intentions from diverse nonverbal information expressed unconsciously. We thus need to investigate the method of speculating partner's intentions by using nonverbal information and to implement it with a robot or agent to realize the smooth communication. However, there is not a system satisfying necessary conditions which we considered for the measuring nonverbal information in natural communication. Therefore, we made a real-time system for readily measuring gaze directions and facial feature points at a time. And then, we established an experimental setting for measuring multimodal nonverbal information that participants expressed during communication. We used the system and setting to make an experiment for discriminating lie, as an example which intentions were unconsciously expressed by nonverbal information. As a result, we found that we could discriminate lies by using diverse nonverbal information in the same way people did