Abstract
In recent years, artificial intelligence based on neural networks has become widespread, but it remains specialized artificial intelligence. On the other hand, Agent Network Architecture (ANA) exists as a planning method that combines both readability of the thought process and immediacy and deliberation of planning required for general-purpose artificial intelligence. However, the network connectivity of ANA has been designed manually in the past. In this paper, we propose a method to automatically generate the coupling relations of ANA networks based on the difference of environmental information before and after an action. In the process of generating network connections, we provide incentives to perform unlearned actions as “curiosity”. Through experiments in a virtual environment, we show that the proposed method can generate network coupling more efficiently than the methods that perform actions randomly or give priority to unlearned executable actions. Furthermore, by applying the proposed method to a real robot, we show that the proposed method can be useful in a real environment by incorporating a mechanism that avoids the execution of error-prone actions.
