A Computational Model of Imitation and Autonomous Behavior

Learning is essential for an autonomous agent to adapt to an environment. One method that can be used is learning through trial and error. However, it is impractical because of the long learning time required when the agent learns in a complex environment. Therefore, some guidelines are necessary to expedite the learning process in a complex environment. Imitation of the behavior of other agents who have already adapted to the environment would shorten an agent's learning time. Thus, imitation can be used by agents as a guideline for learning. In this study, we propose a computational model of imitation and autonomous behavior. We expect that an agent can reduce its learning time through imitation. The actions that an agent performs are represented by a set of features such as the type, location, and object of an action. The agent tends to imitate the similar actions of other agents, and the similarity between actions is calculated, which is indicative of the importance of each feature. The proposed model is evaluated using a dining table simulator. The experimental results indicate that the proposed model can adapt to the environment faster than a baseline model that learns only through trial and error, and that the proposed model can shorten the learning time further if the importance of each feature can be adjusted by learning.