Conveying Intention by Motions With Awareness of Information Asymmetry

Yosuke Fukuchi; Masahiko Osawa; Hiroshi Yamakawa; Tatsuji Takahashi; Michita Imai

doi:10.3389/frobt.2022.783863

Conveying Intention by Motions With Awareness of Information Asymmetry

Front Robot AI. 2022 Feb 16:9:783863. doi: 10.3389/frobt.2022.783863. eCollection 2022.

Authors

Yosuke Fukuchi¹, Masahiko Osawa², Hiroshi Yamakawa^{3

4

5}, Tatsuji Takahashi^{4

5

6}, Michita Imai¹

Affiliations

¹ Faculty of Science and Technology, Keio University, Yokohama, Japan.
² Nihon University, Tokyo, Japan.
³ The Whole Brain Architecture Initiative, Tokyo, Japan.
⁴ School of Engineering, University of Tokyo, Tokyo, Japan.
⁵ RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
⁶ School of Science and Engineering, Tokyo Denki University, Tokyo, Japan.

Abstract

Humans sometimes attempt to infer an artificial agent's mental state based on mere observations of its behavior. From the agent's perspective, it is important to choose actions with awareness of how its behavior will be considered by humans. Previous studies have proposed computational methods to generate such publicly self-aware motion to allow an agent to convey a certain intention by motions that can lead a human observer to infer what the agent is aiming to do. However, little consideration has been given to the effect of information asymmetry between the agent and a human, or to the gaps in their beliefs due to different observations from their respective perspectives. This paper claims that information asymmetry is a key factor for conveying intentions with motions. To validate the claim, we developed a novel method to generate intention-conveying motions while considering information asymmetry. Our method utilizes a Bayesian public self-awareness model that effectively simulates the inference of an agent's mental states as attributed to the agent by an observer in a partially observable domain. We conducted two experiments to investigate the effects of information asymmetry when conveying intentions with motions by comparing the motions from our method with those generated without considering information asymmetry in a manner similar to previous work. The results demonstrate that by taking information asymmetry into account, an agent can effectively convey its intention to human observers.

Keywords: Bayesian theory of mind; PublicSelf model; explainable AI; human-agent collaboration; legible motion; public self-awareness; reinforcement learning.