This paper investigates a hybrid robot that integrates physical and AR body parts, focusing on how the presentation modalities of the head and arms impact human perception of social presence. We implemented a hybrid robot system that allows switching the presentation modalities of the head and arms between physical and AR, and compared four conditions: both physical, physical head and AR arms, AR head and physical arms, and both AR. Two experiments were conducted with different tasks: material explanation and multi-party discussion. These experiments demonstrate that the hybrid robot with a physical head and AR arms effectively overcomes AR device limitations, achieving a social presence comparable to or greater than that of a fully physical robot. The findings also show that the specific AR-presented body parts significantly influence evaluations, underscoring the importance of careful design in hybrid robots.
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 study explores safe and efficient coexistence of autonomous vehicles (AVs) and pedestrians in mixed-use traffic environments. Using an evolutionary game theory framework with imitation and best-response dynamics, respectively, and under the assumption of heterogeneous risk perceptions among pedestrians, spontaneous collective behavioral patterns which can be formed as their interactions are analyzed. Numerical results indicate that fully autonomous decision-making vehicles may better avoid social dilemmas such as the "Freezing Robot Problem" and promote cooperation. The study emphasizes the importance of aligning AV behavior with suitable environmental and traffic rule conditions for the successful integration of AVs into public spaces.
Conversational XAI systems often follow a limited question-answering paradigm, handling user queries independently without tracking evolving understanding or adapting explanations. We present CoCoXplain, a conversational XAI agent that treats explanation as a co-constructed process shaped jointly by explainer and explainee. Built on the MAPE-K loop and implemented with interacting LLM agents, CoCoXplain monitors user comprehension, maintains an explicit user model, and adapts explanation strategies dynamically. The design draws on human-human explanation, dialogue models, and socio-cognitive factors, operationalizing co-construction through monitoring and scaffolding. In an online study with 192 participants, CoCoXplain achieved comparable objective understanding to a click-based baseline while significantly improving subjective understanding, perceived co-construction (monitoring, scaffolding, collaborative construction), and social dimensions such as sociability, trust, usability, and user-agent alliance. These results show that structured adaptive architectures enable explanations to evolve with the user, offering more effective and socially attuned XAI than static information delivery.
Robots are no longer just tools-they are becoming social agents that can shape how we engage with culture. This study examines what influences visitors' perceptions of an autonomous museum guide robot, focusing not only on technical capabilities but also on human-centered factors. In a maritime exhibition, 34 participants interacted with a fully autonomous, LLM-powered robot acting as a museum guide. Using self-report questionnaires, we explored how individual differences - age and prior experience with robots - interacted with experimental conditions to shape participants' impressions of the robot. Our findings suggest that these personal factors significantly affect how visitors evaluate the robot, suggesting that effective design must reflect the diversity of users' experiences and expectations. By acknowledging the complexity of human-robot interaction, we move closer to creating robotic guides that are not only functional but also socially attuned.
