Three-dimensional match-3 puzzle games (Match 3D) are a sub-genre of tile-matching puzzles in which players identify and remove sets of three identical 3D objects. Sustaining long-term player engagement in Match 3D games requires diverse and appropriately difficult levels, yet manual design is resource-intensive. In this paper, we present an on-device level generation model that produces a virtually infinite number of levels with minimal designer intervention. By applying reinforcement learning (RL), our model generates levels designed to meet specified difficulty targets through a reward function based on completion time estimates derived from human play data. We evaluate our system by assessing the accuracy of difficulty matching, the diversity of generated content, and the run-time performance on smartphones via ONNX and Unity Barracuda. Experimental results confirm that the RL approach generates diverse, challenging, and engaging levels, thereby offering a sustainable solution for Match 3D puzzle game design. Furthermore, our proposed system has been seamlessly integrated into a commercial Match 3D game application, providing users with a real-world gameplay experience. To the best of our knowledge, this represents the first successful deployment of PCGRL for on-device, real-time level generation in a commercial mobile puzzle game.

Customer service with teleoperated robots is suscep-tible to the same problems related to stress as is emotional labor in general, such as for in-person customer service representatives. In this study, we aimed to reduce that stress by constructing a buddy-like rapport between the robot, which is the target of teleoperation, and its operator. For this purpose, we designed an empathetic interaction between the robot and the operator and conducted a customer service experiment to verify its effectiveness. Our results demonstrate that the proposed interaction can build rapport between the robot and the operator and the operator can feel more reassured. Although the effect on stress could not be isolated directly from the data, detailed analyses of the response to the questionnaires indicated that the proposed interaction may be useful to relieve stress.

Many people, especially Japanese, are embarrassed to converse with agents such as virtual assistants, probably due to a low social presence, which refers to the degree to which one perceives the human-like properties of an agent. We assumed that poor emotional expressions of agents may impair their human-likeness. In this study, we proposed melodic emotional expression (MEE), which is a new auditory emotional expression for spoken dialog agents. We added background music (BGM) and sound effects as MEE to synthetic voices and conducted experiments to investigate the effects. First, we found that adding MEE to the flat synthetic voice could convey emotions as intended. We also found that when positive emotions were expressed by MEE, it made the agent more human-like and easier to talk to. Furthermore, we achieved these effects when MEE was added to an emotional synthetic voice. These effects were particularly noticeable with the BGM. We further attempted automatic BGM generation, which is necessary for the practical application of MEE. Listeners accurately categorized the BGM generated by the prototype system into four types of emotions: joy, angry, sad, and relaxed.

Combining a device that mediates social touch between people in remote locations with a video call can be expected to enhance the feeling of being in the same space (sense of space sharing) and social bonding with the remote partner. However, when the device is combined with a mobile phone video call, size inconsistency could occur between the device that acts as a substitute for the body part and the reduced video of the partner. In addition, when the device operates separately from a mobile phone in wireless communication, positional inconsistency also happens. This study developed a robotic hand-type social touch device and investigated whether it enhances the sense of space sharing and social bonding even though these inconsistencies occur. Experimental results revealed that the user's belief that the finger movements of the device were the partner's hand movement contributed to the sense of space sharing. The movements of the device according to the flow of conversation facilitated this belief; hence, the sense of space sharing increased in spite of the inconsistencies. Positional inconsistency was also found to have an adverse effect on social bonding. Positional consistency contributes to enhance social bonding because it visually facilitates the user's belief that the device is a substitute for the partner's body. Basically, the functions of the device, such as skin softness, body temperature, finger movements, and grip force, which enhance the reality of the touch sensation, could be effective in facilitating the user's beliefs. However, it was suggested that awareness of temperature does not always make positive contribution to the reality and could lead to eeriness.

In most role-playing games, a non-player character (NPC) tends to have the most initiative, i.e., developing conversation topics, on the dialog. Thus, the player tends to experience a unilateral impression, i.e., the feeling of that an NPC forces conversation topics on the player in spite of the player's willingness, and gradually loses his/her motivation to communicate with an NPC. In order to address this problem, this study proposes a dialog method whereby an NPC can adaptively switch the initiative by determining the player's willingness to talk/listen to the NPC. Based on the results of an evaluation experiment in which the participants talked with the NPC in the game scenario, it is suggested that the aspect of mixed-initiative in our proposed method could decrease the unilateral impression associated with the existing method that an NPC has the most initiative. Furthermore, the determination of the player's desire in our proposed method could further motivate players to talk to the NPC.

The importance of building rapport between a human and an agent is increasing with the burgeoning development of robot technology. Several recent studies have focused on the chameleon effect, using psychological concepts to investigate human-agent interaction. However, the validity of the chameleon effect in human-agent interaction is controversial. Few studies have explored the influence of individual cognitive ability and the rate of mimicry on the human-agent interaction. We explored the optimal rate of mimicry and the relationship between mimicry rate and individual empathic ability. We controlled the amount of agent mimicry and examined the effect on participants classified as high- and low-perspective takers. We found that, overall, participants preferred agents that mimicked their behavior 83% of the time. Moreover, high-, but not low-, perspective takers tended to be influenced by the mimicry rate.

A novel nonlinear foreground calibration of pipeline A/D converters based on LMS algorithm has been proposed and verified by macro-based simulation as well as measurement of test chip. The prototype 23-mW 50-MS/s 10-bit pipeline ADC in 0.13-mum CMOS has confirmed that the proposed calibration can accurately and rapidly correct the inaccuracy caused both by heavy nonlinearity of low-gain op-amps and by their incomplete settling without requiring any dedicated voltage references, complicated structures or timing sequence and hence can be used for various applications.