Model of Spatial Human-Agent Interaction with Consideration for Others

Communication robots often need to initiate conversations with people in public spaces. At the same time, such robots must not disturb pedestrians. To handle these two requirements, an agent needs to estimate the communication desires of others based on their behavior and then adjust its own communication activities accordingly. In this study, we construct a computational spatial interaction model that considers others. Consideration is expressed as a quantitative parameter: the amount of adjustment of one's internal state to the estimated internal state of the other. To validate the model, we experimented with a human and a virtual robot interacting in a VR environment. The results show that when the participant moves to the target, a virtual robot with a low consideration value inhibits the participant's movement, while a robot with a higher consideration value did not inhibit the participant's movement. When the participant approached the robot, the robot also exhibited approaching behavior, regardless of the consideration value, thus decreasing the participant's movement. These results appear to verify the proposed model's ability to clarify interactions with consideration for others.

Cognitive Architecture Toward Common Ground Sharing Among Humans and Generative AIs: Trial on Model-Model Interactions in Tangram Naming Task

For generative AIs to be trustworthy, establishing transparent common grounding with humans is essential. As a preparation toward human-model common grounding, this study examines the process of model-model common grounding. In this context, common ground is defined as a cognitive framework shared among agents in communication, enabling the connection of symbols exchanged between agents to the meanings inherent in each agent. This connection is facilitated by a shared cognitive framework among the agents involved. In this research, we focus on the tangram naming task (TNT) as a testbed to examine the common-ground-building process. Unlike previous models designed for this task, our approach employs generative AIs to visualize the internal processes of the model. In this task, the sender constructs a metaphorical image of an abstract figure within the model and generates a detailed description based on this image. The receiver interprets the generated description from the partner by constructing another image and reconstructing the original abstract figure. Preliminary results from the study show an improvement in task performance beyond the chance level, indicating the effect of the common cognitive framework implemented in the models. Additionally, we observed that incremental backpropagations leveraging successful communication cases for a component of the model led to a statistically significant increase in performance. These results provide valuable insights into the mechanisms of common grounding made by generative AIs, improving human communication with the evolving intelligent machines in our future society.