Conveying Intention by Motions With Awareness of Information Asymmetry

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number783863
JournalFrontiers in Robotics and AI
Volume9
DOIs
Publication statusPublished - 2022 Feb 16

Keywords

  • Bayesian theory of mind
  • PublicSelf model
  • explainable AI
  • human-agent collaboration
  • legible motion
  • public self-awareness
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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