Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

Abstract

We propose a model to achieve human localization in indoor environments through intelligent conversation between users and an agent. We investigated the feasibility of conversational localization by conducting two studies. First, we conducted a Wizard-of-Oz study with N = 7 participants and studied the feasibility of localizing users through conversation. We identified challenges posed by users’ language and behavior. Second, we collected N = 800 user descriptions of virtual indoor locations from N = 80 Amazon Mechanical Turk participants to analyze user language. We explored the effects of conversational agent behavior and observed that people describe indoor locations differently based on how the agent presents itself. We devise “Entity Suitability Scale,” a concrete and scalable approach to obtain information to support localization from the myriad of indoor entities users mention in their descriptions. Through this study, we lay foundation to our proposed paradigm of conversational localization.

Keywords

conversational agents, indoor human localization

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, New Orleans, April 29 - May 5

First Page

1

Last Page

6

ISBN

9781450391566

Identifier

10.1145/3491101.3519617

Publisher

ACM

City or Country

New Orleans, LA, USA

Additional URL

https://doi.org/10.1145/3491101.3519617

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