World state dependent action graph based on knowledge representation using scene graph

Abstract

For intelligent systems, it is important to understand the action possibility for agent in real space. As the action possibility varies with the subsystem configuration of the agent and its states, the possibilities should be understood based on the world state comprising the agent’s state as well as the environmental state. However, most conventional methods consider only the environmental state. Therefore, this study proposes a world state-dependent action graph based on knowledge representation using scene graphs to achieve the objective, which allows the capture of action possibility of agents and their recursive variations with the world state. Moreover, the effectiveness of the proposed method was verified with simulations, assuming a coffee shop environment.