Navigating social dilemmas transparently is essential for social artificial agents, especially when resource values are subjective. We propose a model where agents act based on three values: self-value, estimated value to another, and the "looking-glass self" (the other’s estimate of the self’s value). We tested this model in a simplified Rummy game by optimizing three agent types (self-oriented: S, socially-oriented: SO, and socially-oriented with looking-glass self: SOL) in competitive and cooperative simulations. Results indicate that SO performed best in competition, while SOL was superior in cooperation. This study demonstrates that a simple, value-based model with a transparent computational process can yield distinct, context-appropriate strategies, providing a promising approach for building more understandable artificial agents.

This workshop aims to support early-career researchers in the field of Human-Agent Interaction (HAI), especially those from non-native English-speaking backgrounds. Despite increasing global engagement, many talented researchers face challenges in presenting their work internationally due to language barriers and limited opportunities. By providing a platform to present research in English in a supportive and peer-driven environment, this workshop will foster accessibility, visibility, and confidence among participants. Presentations will be followed by extended and moderated Q&A sessions to encourage inclusive discussion. The workshop also invites guest talks from early-career researchers who exemplify innovative HAI research, promoting role models for the next generation. Overall, the workshop seeks to lower entry barriers and empower underrepresented voices in the global HAI community.

In recent years, communication between humans and interactive robots has increased, even though their conversations are not always one-on-one. Sometimes, robots accept newcomers or actively invite them to participate. In such situations, the robot needs to estimate the internal states of both the existing participant and the newcomer, and determine whether to accept or reject the latter’s participation based on its estimations of their internal states. In this study, we explored how humans estimate the internal states of others based on body torque, which is expressed as a combination of face and body directions in new participant situations. Unfortunately, how humans infer internal states from physical signals such as the body torques of current and new participants and how they behave has not been studied. In this study, we analyze the effects of body torque on human behavior based on the results of field experiments conducted at the National Museum of Emerging Science and Innovation (Miraikan). Our results suggest that the body direction of a robot affects human behavior in new participation situations.

The physical signal called body torque, expressed as a combination of face and body directions, are likely to be effective in a newcomer situations in dialogues with three or more participants (multi-person conversation). Newcomers in a multi-person conversation infer the current participants’ internal states from their physical signals and decide when and to what extent they will be allowed join the conversation. However, insufficient research has been conducted on the agent’s design of behavior to encourage or discourage participation in conversations based on the current and new participants. This paper analyzes the effects of body torque on new participants based on the results of a field experiment at the National Museum of Emerging Science and Innovation (Miraikan). Our results suggest that current participants’ head and body directions independently and interactively affect a newcomer’s behavior.

People have always perceived machines to be entities that perform tasks based on explicit instructions from humans, so it was assumed that machines themselves would not behave autonomously according to their own independent purposes. Therefore, people do not understand how to manage interactions with machines when they use information from their surroundings or behave autonomously and purposely without instructions from humans. People are often negatively affected, by experiencing resistance or vigilance, in their interactions with machines that behave like people. In this study, we focused on the interaction between a person and an agent using a helping situation as the context. In the experiment, we observed the cognitive responses of the recipient to the helping agent in the helping situation and evaluated how people viewed the agents. The results showed that people were likely to perceive the agent as a person, even when the helper was an agent. Based on the findings from the experiments, we discussed how people can appropriately handle interactions with agents and how an agent should be designed to achieve good interactions with people.

This paper presents a novel location matching scheme under severe multipath environments. The target scenario is battery boards with multiple battery modules. The proposed scheme utilizes the polarization to generate RSSI distributions. Measurement based evaluation was conducted for two battery boards. The estimation results show that the accuracy is 57% and 76% for battery boards 1 and 2, respectively, which is higher than the target for comparison. In addition, the proposed method is able to detect suspicious estimation locations.

We propose an agent model that determines its behavior from an internal state and a spatial relationship with a target to generate approaching and avoiding behaviors in encounter scenes. This model is based on the relationship with an opponent rather than with a scenario. The agent moves to increase the utility value obtained from the preferences for both aggressive and passive involvement. We analyzed the behavioral and utterance data of human–human interactions based only on two-dimensional position information by simple-shaped robots. The rate of participants’ behavior following the model was significantly higher than that of a random walker. Based on this result, we estimated the internal state during the interactions from the behavior of the participants and analyzed it. The words uttered by one member of a pair correlated with the internal state estimated from the behavior of the other member of the pair. The frequency of the internal states observed from the participants who were recommended to interact with the partner was different from that observed from the participants who did not receive such recommendations. These results suggest that a model with two preferences can approximate a human’s internal state in encounter scenes.

Human-Agent Interaction (HAI) research covers a wide range of agents, including the modeling of human cognitive processes and the design of agents? cognitive processes. However, no framework for organizing the wide range of research areas has yet been concretely developed. In this study, we propose a framework for the design of Human-Agent Interaction. This framework is described as a system consisting of a set of variables that affect HAIs and a function that represents the relationship between these variables. We listed what papers were accepted at the international conference on Human-Agent Interaction in 2019 following a framework of variables on which each study focuses. We argued that approaches that connect variables and elaborate models of human cognitive processes are important for future HAI research.

To construct a general theory in human-agent interaction (HAI), it is essential to discuss how to generalize from models within the research area. In this study, we propose a formulation of HAI for the establishment of axiom systems. The environment for agents, behavior as a time change in the environment, and the internal state as an evaluation of the environment are identified as basic variables. The relations of these variables are expressed by functions. Ideally, HAIs should be explained in one axiomatic system to discuss the similarities and differences among human-human interaction, human-animal interaction, and human-robot interaction. For this purpose, we need to verify the model theoretically in a future study.

To design agent behavior in the initial phase of communication, we propose an agent model that possesses preferences for a relationship with a target. As an internal state, this model considers two relationship preferences with a target: control and acceptance. The agent's spatial behavior, which is represented by a change in its position, is determined based on its internal state and its positional relationship with the target. We investigated the interactions caused by the combination of the internal states of two agents through a computer simulation. We simplified the combinations of the internal states for the agent to reach its preferred position and clarified the position combinations that become unreachable. Based on these results, we discussed the change of the internal state based on rationality. In this model, since the target's internal state is directly expressed by its behavior, the agent can adjust its own internal state based on rationality without estimating the target's internal state.

Getting clues about how to engage with others is quite difficult when the persons involved are mutually unknown and unrelated to each other: strangers, in other words. In fact, those who distribute leaflets or free samples of various goods to strangers in towns or on city streets seem to have a special manner of interacting with others. They can naturally experience success by handing out something without any extra precaution. This study focuses on such interaction that naturally starts by engaging with others. We explored the physical interaction in an experimental field between a person (P1) and an engrossed person (P2) by asking this question: how should P1 approach P2. Our result shows that P1 changed his/her route to approach P2 based on how engrossed P2 is with a book. This result indicates that getting clues for engaging with others must consider the mutual attitudes about how each person wants to interact with another based on two-dimensions: controlling the another and being accepted by another.

Humans can communicate because they adapt and adjust their behavior to each other. We hypothesize that developing a relationship with others requires coordinating the desire to communicate and that this coordination is related to agency identification. To model this initial phase of communication, we created an experimental environment to observe the interaction between a human and an abstract-shaped robot whose behavior, moving on the floor and rotating, was mapped by another human. The participants were required to verbalize what they were thinking or feeling while interacting with the robot. At present, we do not have a sufficient number of participants, and experiments and data analysis are ongoing. We must verify the effects of interaction patterns and inspect what type of action and reaction are regarded as signals that enhance interpersonal interaction.

Humans can communicate because they adapt and adjust their behavior to each other. Developing a relationship with a robot, on the other hand, is difficult, especially with a robot that humans have never met before. However, little investigation has addressed interaction when no information about the interaction partner has been provided and with no experimental task. We must clarify how people perceive unknown objects as agents through interaction. We created an experimental environment to observe the interaction between human and a robot whose behavior was actually mapped by another human. The participants verbalized what they were thinking or feeling while interacting with the robot. The results of our experiment suggest that the timing of the movements was used as a cue for interaction development. We need to verify the effects of other interaction patterns and inspect what kinds of actions and reactions are regarded as signals that enhance interpersonal interaction.

Humans can communicate because they adapt and adjust their behavior to each other. Developing a relationship with an unknown artifact, on the other hand, is difficult. To address this problem, some robots utilize the context of the interaction between humans. However, there has been little investigation on interaction when no information about the interaction partner has been provided and where there has been no experimental task. Clarification of how people perceive unknown objects as agents is required. We believe that a stage of subconscious interaction plays a role in this process. We created an experimental environment to observe the interaction between a human and a robot whose behavior was actually mapped by another human. The participants were required to verbalize what they were thinking or feeling while interacting with the robot. The results of our experiment suggest that the timing of movement was used as the cue for interaction development. We need to verify the effects of other interaction patterns and inspect what kind of action and reaction are regarded as signals that enhance interpersonal interaction.

This paper reports an analytic finding in which humans inferred the emotional states of a simple, flat robot that only moves autonomously on a floor in all directions based on Russell's circumplex model of affect that depends on human's spatial position. We observed the physical interaction between humans and a robot through an experiment where our participants seek a treasure in the given field, and the robot expresses its affective state by movements. This result will contribute to the basic design of HRI. The robot only showed its internal state using its simple movements.

Humans can interact with strangers because they can communicate with each other. On the other hand, developing a relationship with an unknown artifact is difficult. In order to address this problem, existing studies have explored various approaches to the artifact's behavioral design. However, little research has been done on interaction where there is no information about the interaction partner and where there has been no experimental task. Clarification of how people regard unknown objects as interaction partners is required. We believe that a stage of subconscious interaction plays a role in this process. We created an experimental environment to observe the interaction between a human and a robot whose behavior was actually mapped by another human. We observed this interaction under two different conditions; where the participants knew and where the participants did not know that the robot could interact. Both sets of participants approached or avoided the robot, but differences in the interaction property for each condition was confirmed. The results of our experiment suggest that a stage of subconscious interaction does exist for recognition of artifacts as interaction partners.

Timing synchronization is important for collaborating machines. For various reasons concerning practicability, some machines cannot have cables to synchronize them. To synchronize wirelessly connected machines, this paper proposes a practical and cost-effective cyclic pulse synchronization system. The proposed system consists of a master machine with an IEEE 802.11 wireless LAN AP and slave machines with wireless LAN STAs. The master machine outputs a cyclic pulse to the AP. Then the STAs can reproduce synchronized cyclic pulses for the slave machines. Thus the machines can synchronize their work timing using the cyclic pulses. All functions to synchronize the pulses are implemented in a wireless LAN chip as user software applications without additional hardware. We evaluated the pulse synchronization precision of our system in the case of the peak network load. The proposed system achieves approximately 200 μs precision for the maximum pulse edge delay of STA output pulse to AP input pulse.

In the next-generation wireless networks, system convergence progressed towards the realization of seamless and ubiquitous environment with software defined radio (SDR). This manuscript proposes a soft-prioritization based system selection algorithm performed by SDR terminal and investigates the effectiveness of the soft-prioritization based system selection by using a concrete simulation model in the practical channel model. To maximize the quality of service (QoS), wireless communication systems are prioritized on the basis of criteria for system selection such as data rate, channel quality and cost, and those systems should be dynamically changed. However, frequent inter-system handovers based on hard-prioritization are undesirable in view of interrupting and dropping communication, particularly for real-time traffics. Wireless communication systems are softly prioritized in the soft-prioritization based system selection algorithm, and therefore inter-system handovers between systems with the same priority aren't initiated. To elucidate the validity of the soft-prioritization based system selection algorithm, a system simulation model consisting of five wireless communication systems is employed. Simulation results confirm that the soft-prioritization system selection algorithm offers higher performance in terms of the number of inter-system handovers and average throughput of best effort traffic

In this manuscript, an interferential packet detection scheme in IEEE 802.11 WLANs is proposed. If another basic service set (BSS) is overlapping domestic BSS, some stations (STAs) may suffer from interference from a hidden terminal in overlapping BSS (OBSS). One of the best ways to avoid this undesirable situation is channel switching after detecting OBSS. However there are some difficulties to recognize existence of OBSS. One difficulty is that STAs in domestic BSS can't receive frames from OBSS correctly and can't check BSSID of frames if traffic in domestic BSS is heavy and if transmission rates of frames from OBSS are higher. Another difficulty is that if the cell radius of domestic BSS is smaller than that of OBSS, some STAs in OBSS may transmit frames asynchronously and interfere with transmissions in domestic BSS. If interference causes frame error, domestic STA can't distinguish interference from degradation of channel condition. This paper proposes a method whereby STAs can detect interferential packet even while STAs receive frames from domestic BSS. If STAs detect interference, channel switching is performed dynamically. The probability of detecting interferential packets is evaluated by computer simulation, and the results confirm the effectiveness of the proposed method