Social Skill Acquisition Model through Face-to-Face Interaction: Local Contingency for Open-Ended Development

Abstract

Behavior associated with joint attention is among the most important human functionalities for communicating with others. Previous studies indicate that even a robot can learn these behavioral patterns as social skills through interaction with a modeled/real caregiver by contingency evaluation. However, existing mechanisms are too time-consuming, especially for implementation on a real-world interactive robot. Also, they are poor in the acquisition of complex skills. In this paper, we propose a fast mechanism that enables the acquisition of many complex social skills within a short interaction time. The mechanism is realized by the utilization of two significant ideas: evaluating contingency locally, and acquiring social skills by finding synergistic contributions of values in contingencies. A comparison of our proposed mechanism in a simple environment of computer simulation with other mechanisms in terms of speed, accuracy, complexity, and noise resistance confirms the superior performance of our mechanism. Further, experimental results obtained with the proposed mechanism in a more complex computer simulation environment, which more closely resembles a real-world environment, indicate that the mechanism can be applied in real-world interaction between a robot and a human.