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.

翻译：为处理智能体引发的环境非预期变化，我们提出了一种环境中心主动推理方法EC-AIF，该方法从环境出发定义主动推理的马尔可夫毯。在传统主动推理中，马尔可夫毯是从智能体出发定义的：首先将执行"动作"的实体（如机器人或人）定义为智能体，然后将直接受智能体"动作"影响的其他人员或物体定义为环境，智能体与环境之间的边界则被定义为马尔可夫毯。这种以智能体为中心的定义方式无法使智能体响应由定义环境之外因素引起的环境非预期变化。在所提出的EC-AIF中，不存在与智能体对应的实体。环境包含所有可观测事物，既包括传统意义上的环境要素（人员与物体），也包括执行"动作"的实体（如机器人和人）。相应地，所有状态（包括机器人和人的状态）均被纳入推理目标，从而消除了环境中的非预期变化。我们将EC-AIF应用于机械臂系统，并通过机械臂物体搬运任务进行验证。实验结果表明，机械臂在成功搬运物体的同时，能够有效响应物体目标位置的变化以及另一机械臂姿态的变化。