Takeshi Kato

AI
3papers
10citations
Novelty32%
AI Score21

3 Papers

ROAug 23, 2024
Environment-Centric Active Inference

Kanako Esaki, Tadayuki Matsumura, Takeshi Kato et al.

To handle unintended changes in the environment by agents, we propose an environment-centric active inference EC-AIF in which the Markov Blanket of active inference is defined starting from the environment. In normal active inference, the Markov Blanket is defined starting from the agent. That is, first the agent was defined as the entity that performs the "action" such as a robot or a person, then the environment was defined as other people or objects that are directly affected by the agent's "action," and the boundary between the agent and the environment was defined as the Markov Blanket. This agent-centric definition does not allow the agent to respond to unintended changes in the environment caused by factors outside of the defined environment. In the proposed EC-AIF, there is no entity corresponding to an agent. The environment includes all observable things, including people and things conventionally considered to be the environment, as well as entities that perform "actions" such as robots and people. Accordingly, all states, including robots and people, are included in inference targets, eliminating unintended changes in the environment. The EC-AIF was applied to a robot arm and validated with an object transport task by the robot arm. The results showed that the robot arm successfully transported objects while responding to changes in the target position of the object and to changes in the orientation of another robot arm.

AIAug 7, 2023
Generative AI trial for nonviolent communication mediation

Takeshi Kato

Aiming for a mixbiotic society that combines freedom and solidarity among people with diverse values, I focused on nonviolent communication (NVC) that enables compassionate giving in various situations of social division and conflict, and tried a generative AI for it. Specifically, ChatGPT was used in place of the traditional certified trainer to test the possibility of mediating (modifying) input sentences in four processes: observation, feelings, needs, and requests. The results indicate that there is potential for the application of generative AI, although not yet at a practical level. Suggested improvement guidelines included adding model responses, relearning revised responses, specifying appropriate terminology for each process, and re-asking for required information. The use of generative AI will be useful initially to assist certified trainers, to prepare for and review events and workshops, and in the future to support consensus building and cooperative behavior in digital democracy, platform cooperatives, and cyber-human social co-operating systems. It is hoped that the widespread use of NVC mediation using generative AI will lead to the early realization of a mixbiotic society.

CYOct 5, 2021
Social Co-OS: Cyber-Human Social Co-Operating System

Takeshi Kato, Yasuyuki Kudo, Junichi Miyakoshi et al.

The novel concept of a Cyber-Human Social System (CHSS) and a diverse and pluralistic 'mixed-life society' is proposed, wherein cyber and human societies commit to each other. This concept enhances the Cyber-Physical System (CPS), which is associated with the current Society 5.0, a social vision realised through the fusion of cyber (virtual) and physical (real) spaces following information society (Society 4.0 and Industry 4.0). Moreover, the CHSS enhances the Human-CPS, the Human-in-the-Loop CPS (HiLCPS), and the Cyber-Human System by intervening in individual behaviour pro-socially and supporting consensus building. As a form of architecture that embodies the CHSS concept, the Cyber-Human Social Co-Operating System (Social Co-OS) that combines cyber and human societies is shown. In this architecture, the cyber and human systems cooperate through the fast loop (operation and administration) and slow loop (consensus and politics). Furthermore, the technical content and current implementation of the basic functions of the Social Co-OS are described. These functions consist of individual behavioural diagnostics, interventions in the fast loop, group decision diagnostics and consensus building in the slow loop. Subsequently, this system will contribute to mutual aid communities and platform cooperatives.