This study develops a practical learning support system to mitigate the psychological and emotional burden on caregivers supporting the increasing population of people with dementia in home-care settings. To address the insufficient fidelity (or "sense of urgency") in conventional learning systems, we propose an interactive, role-playing learning system featuring a dementia patient agent. This system leverages "LLM Embedded in Cognitive Architecture (LEC)," a novel framework that integrates a large language model (LLM) with a cognitive architecture to accurately interpret user intent. This role-playing promotes realistic caregiving communication by generating the agent’s responses based on the user’s spoken dialogue. Testing with caregiving novices indicated a statistically significant improvement in both learning effectiveness and the quality of understanding, thereby demonstrating the system’s potential to enhance learning.

Part-time workers are seen handing out pocket tissues containing advertisements to pedestrians in the streets and around train stations in Japan. However, there are cases in which pedestrians feel stressed by workers handing out tissues. This is when the tissues are unnecessary for pedestrians. Workers actively try to hand out tissues to pedestrians because if they do not hand out tissues, their work will be evaluated negatively. Most pedestrians understand that refusing to accept a tissue is a betrayal of the worker’s expectations. Therefore, refusing to accept a tissue is emotionally taxing for the pedestrian. The authors believe that anthropomorphic agents handing out tissues can alleviate the aforementioned mental burden for pedestrians. In this paper, we conducted an experiment in which pedestrians interacted with a tissue-distributing worker by reproducing a typical Japanese cityscape in VR. The results showed that the anthropomorphic agent handing out tissues may cause less stress to pedestrians than a human worker handing out tissues. The results of this paper suggest the usefulness of anthropomorphic agents for tasks such as distributing advertisements in the city and around train stations, and for fundraising activities.

Soon, autonomous cars are expected to become widespread, but at present, it is still common for people to drive cars manually. A driving support agent (DSA) is used to support driving using the human-agent interaction approach. In previous research, it was shown that DSAs can be useful in assisting the user through voice utterances. However, previous studies focusing on DSA utterance design have not compared off-record strategies with other politeness strategies that explicitly communicate the speaker's intentions, and it was not clear whether DSA should provide explicit utterances to the user. Therefore, in this study, a video-based subjective evaluation experiment (n=240) was conducted to compare the acceptability of off-record strategies, positive politeness strategies, negative politeness strategies, and direct utterances without politeness strategies. The results of the experiment showed that the negative politeness strategy was evaluated significantly higher than the off-record strategy on evaluation items related to functionality. This result suggests the usefulness of providing explicit instructions with linguistic consideration (politeness) when DSA provides driving assistance to users. In addition to the above findings, there were several correlations between the user's personality characteristics and the subjective evaluation of the DSA. Specifically, there was a significant positive correlation between the level of conscientiousness of the user and the direct utterance evaluation value. For the positive politeness strategy and off-record strategy, there were no significant correlations between evaluation categories of the dislikeability and users' personality characteristics. These results suggest that individual differences in n egative impressions due to personality characteristics are small in the positive politeness and off-record strategies.

The purpose of this research is to construct a dialogue model using proper compliments by an agent. This paper reports compliment expression by communication robot in the conversation necessary to construct dialogue model. Compliments are one of the important verbal behavior for having human communication. Humans use compliments to build and maintain relationships in daily life, taking into account social relationships and psychological states. Several research has proven that compliments used by humans in daily life is constructed several syntactic patterns. If the agent is able to use properly these compliment patterns, the agent can build an affinity relationship with human. However, few research has been done about effects of formulated compliments by agents on humans. The purpose of the experiment of this research is to investigate the effect of agents on impressions of formulated compliments and on the construction of relationships. The comparative experiment was conducted to verify the psychological subjective evaluation of dialogue interaction between the agent giving compliments and the agent not giving compliments. Experimental results showed that formulated compliments by the agent gave a positive impression to humans and was recognized as the compliment. However, there was no significant differences in the psychological impression of humans from the expression of compliments, indicating that it is necessary to construct the flexible compliment dialogue system using fuzzy control for the expression of compliments.

The purpose of this research is to analyze overconfidence and dependence caused by receiving the utterance from a driving support robot when a driver drives a car. Comfortable driving can be performed by receiving support from the driving support robot. However, it is expected that drivers will lead to accidents by the dependence or overconfidence bias on support robots. The dependence bias is that driver decreases the quality of driving behavior with respect to the function of the safety system, and the overconfidence bias is that driver embraces expectations more than the performance of the driving support robot. Although this problem includes the fuzziness of human characteristics, the conventional driving support system does not take into consideration such influence of the overconfidence or dependent bias. In order to verify whether driver has the bias against driving support robot in this paper, we investigated the impression of the robot when the driving support with driving animation and robot and report the results.

In this study, we aim to develop an educational environment and platform based on color science, merging information technology, robotics and artificial intelligence. In the educational environment, the robot requires functional capability for human-like communication. However, the robot may have difficulty in grasping the small cues to represent feelings and emotional changes of human by itself. This paper presents a measurement system with pneumatic pressure sensor for heart rate variability analysis. We proposed a fuzzy spiking neural network to extract heartbeat signal from the measured data. We furthermore conducted experiments to investigate the effects on student's psychological states while solving some conundrums by using the measurement system.

“Are you a werewolf?” is one of the most popular communication games and is played globally. The AIWolf Project developed an agent, named “the AIWolf,” that can play “Are you a werewolf?”. An AIWolf utters its thoughts using an AIWolf Protocol. As it is difficult for humans to understand the AIWolf Protocol, translation into natural language is required when human players are involved. However, the conventional method of translation uses a word-to-word method, creating the impression that the utterances have been generated by a machine. This study aimed make the utterances of AIWolf sound more human. The authors set the target that a human player would be unable to distinguish human speech from that generated by AIWolves (the Turing test). The authors define the situation as the maximum value of humanity. The output of translated AIWolf Protocol was paraphrased using data from Werewolf BBS Logs. This study considers making the utterances of AIWolf sound more human using Werewolf BBS Logs and a possibility assignment equation with fuzzy sets. In this paper, an experiment was conducted to confirm whether paraphrasing the utterances of AIWolf using Werewolf BBS Logs for human-like speech is useful or not. It was shown that the experimental method produced slightly more human-like speech than the conventional method.

In interpersonal interactions, humans speak in part by considering their social distance and position with respect to other people, thereby developing relationships. In our research, we focus on positive politeness (PP), a strategy for positively reducing the distance people in human communication using language. In addition, we propose an agent that attempts to actively interact with humans. First, we design a dialog system based on the politeness theory. Next, we examine the effect of our proposed method on interactions. For our experiments, we implemented two agents:the method proposed for performing PP and a conventional method that performs negative politeness based on the unobjectionable behavior. We then compare and analyze impressions of experiment participants in response to the two agents. From our results, male participants accepted PP more frequently than female participants. Further, the proposed method lowered the perceived sense of interacting with a machine for male participants.

In this research, we target at the interactive communication game “werewolf” with a subject of research. Werewolf is a popular party game all over the world, and the relevance studies have been advanced in recent years. However, the life-like agent who does werewolf has not been developed. Therefore the purpose of this research is to analyze non-verbal information from movies which play the werewolf with face-to-face communication and to make clear the impression for others by the movements of players in the game. Moreover, we verify whether the life-like agent gives an impression like human in werewolf game by mounting the movements on a life-like agent.

Werewolf is one of the popular communication games all over the world. It treats ambiguity of human discussion including the utterances, gestures and facial expressions in a broad sense. In this research, we pay attention to this imperfect information game werewolf. The purpose of the research is to develop an intelligent agent “AI werewolf” which is enabled to naturally play werewolf with human. This paper aims to investigate how behavior contribute to victory of own-side players by using machine learning as a first step. As the results of investigation and analysis of the playing movie, we found that nonverbal information in the game of werewolf has importance to winning or losing the game.

"Are You a Werewolf?" is a conversation type game. We construct a Werewolf match system for humans with lifelike agents. We evaluate whether it is possible to realize conversation space of "Are You a Werewolf?" with the system.

The first international workshop on mood engineering (IWME) is held in Tsukuba, Japan on October 28, 2014 and co-located with the second international conference on human-agent interaction (HAI). The main objective of the workshop is to create artificial moods by robot and agents and to clarify the mechanism of a mood created by a group including humans, robo ts, and agents. We have constructed an exciting program of demonstrations, posters, and video materials as interactive sessions that will provide participants creative a mood for collaborative research.

This paper investigates that human tend to percept of humanness and individuality to what features of human motion. We analyze from experiments based on test subjects how recognize of individuality by comparing a developed own self motion with other motion made by motion capture using Kinect sensor. The results showed except for the subjects could not perform the movement extremely distinctive, many subjects to recognize the movement of their own. In addition, the respondents also recognized with a video camera an objective of its own motion, the subjects of some of their own and do not match the image of the inside of me. It is difficult to percept humanness based on less-accurate expression of human motion.

In this paper, we pay attention to strong atmosphere of a group momentarily constructed by a place because the agent who expresses gesture and face expression gathers plurals. We called the atmosphere socially constructed as group pressure of agents. The purpose of the paper is to develop a group pressure and investigate the influence to human. We verify if the group pressure made by multiple life-like agents exert an influence on the judgment of player by using the cross-cultural simulation game we developed before.

In this paper, we investigated the relationships between the behavior of a player in a cross-cultural simulation game and the social skills of a player by a scale of social skills for the comprehension of social adaptive behavior of human in a social group. The game developed adopted KiSS-18 as a scale of social skills. We showed the relationships between the changes of a player's behavior and the measured results by KiSS-18, and discussed the feasibility of the determination of social adaptive skills by interactions with fourteen agents in the game.

Recently, researches of human-agent interaction (HAI) are popular toward autonomous agents to act and cope in various human societies. If agents adapt to humans' society, they have social skills. In this paper, we introduce how developing a social adaptive agent. There are many rules in societies. Therefore, social agents should have a social skill which is obtaining social rules. We develop agents adapting group as social adaptive agents. We improve some functions from a simulation game: Online BARNGA from BARNGA to observe humans' behavior. We observe humans' behavior in Online BARNGA. As a result, we find that humans have transitions of three inner states with rdquonoticerdquo and rdquobehaviorrdquo to get implicit rules, and rules fall into two categories: a byelaw and an ethic. In Online BARNGA, a byelaw is a rule of card game, an ethic is a strategy. Based on the above results, we develop agents with transitions of three inner state with rdquonoticerdquo and rdquobehaviorrdquo and two type learning modules. Furthermore, we analyze agents' behavior in online BARNGA, and compare humans' that. As a result, we confirm that agent adapt socially as if it were human.

We have been worked about robotic social imitation in order to learn self-behavior depending on self-embodiment through interaction with multiple human. In this paper, we propose a learning method which allows to select behavioral patterns depending on self-embodiment and self-evaluation from multiple instructors by using the robot simulator Webots. We confirmed that results demonstrated that our proposal allows to improve the representative teaching data by the clustering includes two evaluation values (the distance moved forward and the impact shock for the body).

Recently, the researches of social robots and agents that smoothly communicate to human are paid to attention. The conventional researches seem to have aimed at the achievement of the society in the meaning of the wide sense. In a word, it is an approach where the designer embedded social competence like human. In this paper, we propose model-based multi user learning agent (M-MULA) to interact with various types of people effectively towards realization of social interaction. M-MULA is equipped with two functions for social learning. One is direct learning function by individualizing of user parameters, and another is indirect learning function based on past experience and the similarity between each users. We confirm proposed method in computer simulation to evaluate the effectiveness

We propose a method for estimating the credibility of the posted information from users. The system displays these information on the map. Since posted information can include subjective information from various perspectives, we can't trust all of the postings as they are. We propose and integrate factors of the user's geographic posting tendency and votes by other users.

This paper describes an overview of an online training support system for ADR mediators. To educate good mediators, much training is necessary, which is not easy for supervisors. As supervisors have to take care of many students, they cannot spare much time for a specific student. To train students effectively, some support system is needed.This system provides an environment for online disputation. Using this system, the supervisor and students can participate in the mediation process even if they are outside of the University. Furthermore, this system stores many disputation records in the form of XML documents as a case base, and this case base is used to navigate the mediation process. During the disputation, users can retrieve old similar scenes of disputation, and they can construct proper arguments by referring to similar scenes. Furthermore, by comparing records of disputation or by analyzing them statistically, we can get the information that help to evaluate the mediation skill.

In an application of reinforcement learning to real-world problems, the function approximators are usually used to approximate the value function and the policy function. It is necessary to construct the function approximator adaptively, because the value function and the policy function change along with the progress of reinforcement learning. In this work, we propose self-organizing basis network (SOBN) which is the method that constructs a function approximator using basis functions adaptively. The proposed method constructs a basis function network by connecting neighbor bases using edges. This basis function network constrains the activating region of each basis function, and the network is modified by updating the location of each basis. Using this mutual dependence, which we call the interaction between basis functions, for searching appropriate architecture of a function approximator, SOBN self-organizes the function approximator. Assuming that the method is applied to reinforcement learning, we apply the method to the function approximation problem, and evaluate approximation performance and convergence time.

In this paper, we describe influence of viewpoints of observation in an interactive evolutionary robotics system. We have been proposed a behavior learning system ICS (Interactive Classifier System) using interactive evolutionary computation. In this system, a mobile robot is able to quickly learn rules by direct teaching of a human operator. ICS is a novel evolutionary robotics approach using a classifier system. We classify teaching methods into internal observation and external one, and investigate influence of observation methods. We have experiments based on our teaching methods in two kinds of tasks. We found that teaching methods from different viewpoints of observation change teaching efficiency because of the difference between a robot's recognition and an operator's one in an environment.