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.

Highlights

  • Theory of mind is the ability to infer other people’s mental states, such as their beliefs, desires, and intentions, from their actions

  • We develop a method for generating such motions by extending the PublicSelf model and compare the generated motions with those that do not consider information asymmetry in an approach similar to that in previous work

  • We found significant improvements from PublicSelf compared to FalseProjective in situations with information asymmetry but did not in situations without information asymmetry

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Summary

Introduction

Theory of mind is the ability to infer other people’s mental states, such as their beliefs, desires, and intentions, from their actions. The targets of theory of mind include other humans but sometimes artifacts (Gergely et al, 1995; Schellen and Wykowska, 2019), regardless of whether they possess mental states similar to those of humans. This phenomenon can be utilized to facilitate natural and efficient. The ability to understand and predict a human coworker’s behavior can help robots to effectively collaborate with people (Lasota and Shah, 2015; Huang and Mutlu, 2016). Human agents should be able to better understand their coworker agents’ future behavior

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