Proposition of affordance extraction from Foundation model for autonomous agent

Abstract

The creature can perceive various information intuitively from physical entities, understand their surroundings, and act adaptively. In planning for autonomous agents, utilizing affordances like living organisms to adapt to the environment and achieve goals efficiently is effective. Therefore, the purpose of this study was to extract affordance information from large-scale language models. Large-scale language models have learned knowledge from a vast amount of text written by humans and can output new text using that knowledge. Thus, it is considered that large-scale language models contain common sense and implicit knowledge that humans possess. In this study, we analyzed the output from the large-scale language model GPT-3 and constructed a knowledge network by extracting knowledge from it. The experiments showed that using this knowledge network enables the acquisition of affordances similar to those of humans.