Affinity Diagramming with a Robot

Author:

Law Matthew V.1,Nwagwu Nnamdi2,Kwatra Amritansh2,Lee Seo-Young2,Diangelis Daniel M.2,Yu Naifang2,Gonzalez-Pumariega Gonzalo2,Rajesh Amit2,Hoffman Guy2

Affiliation:

1. Denison University, Granville, USA

2. Cornell University, Ithaca, USA

Abstract

We investigate what it might look like for a robot to work with a human on a need-finding design task using an affinity diagram. While some recent projects have examined how human–robot teams might explore solutions to design problems, human–robot collaboration in the sensemaking aspects of the design process has not been studied. Designers use affinity diagrams to make sense of unstructured information by clustering paper notes on a work surface. To explore human–robot collaboration on a sensemaking design activity, we developed HIRO, an autonomous robot that constructs affinity diagrams with humans. In a within-user study, 56 participants affinity-diagrammed themes to characterize needs in quotes taken from real-world user data, once alone and once with HIRO. Users spent more time on the task with HIRO than alone, without strong evidence for corresponding effects on cognitive load. In addition, a majority of participants said they preferred to work with HIRO. From post-interaction interviews, we identified eight themes leading to four guidelines for robots that collaborate with humans on sensemaking design tasks: (1) account for the robot’s speed, (2) pursue mutual understanding rather than just correctness, (3) identify opportunities for constructive disagreements, and (4) use other modes of communication in addition to physical materials.

Funder

Cornell University Center for Advanced Computing

Civil, Mechanical and Manufacturing Innovation Program of the National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Reference74 articles.

1. Conducting semi-structured interviews;Adams William C.;Handbook of Practical Program Evaluation,2015

2. Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

3. Stay on the Wikipedia task: When task-related disagreements slip into personal and procedural conflicts

4. Pranjal Awasthi, Maria Balcan, and Konstantin Voevodski. 2014. Local algorithms for interactive clustering. In International Conference on Machine Learning. PMLR, 550–558.

5. How Does Cognitive Conflict in Design Teams Support the Development of Creative Ideas?

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3