Effect of cognitive biases on human-robot interaction: A case study of a robot's misattribution

M. Biswas, J. Murray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a model for developing long-term human-robot interactions and social relationships based on the principle of 'human' cognitive biases applied to a robotic platform. The aim of this work is to study how a robot influenced with human 'misattribution' can help to build better human-robot interactions than unbiased robots. The results presented in this paper suggest that it is important to know the effect of cognitive biases in human characteristics and interactions in order to better understand how this plays a role in human-human social relationship development for the purpose of developing robot-human interactions and relationships. The results presented in this paper show how a single cognitive memory bias i.e. misattribution in robot-human verbal communication allows for better human-robot interaction than similar robot-human communication without this misattribution bias.
Original languageEnglish
Title of host publicationThe 23rd IEEE International Symposium on Robot and Human Interactive Communication
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1024-1029
Number of pages6
ISBN (Electronic)978-1-4799-6765-0
ISBN (Print)978-1-4799-6763-6
DOIs
Publication statusPublished - 20 Oct 2014
Event23rd IEEE International Symposium on Robot and Human Interactive Communication - Edinburgh, United Kingdom
Duration: 25 Aug 201429 Aug 2014

Publication series

Name
PublisherIEEE
ISSN (Print)1944-9445
ISSN (Electronic)1944-9437

Conference

Conference23rd IEEE International Symposium on Robot and Human Interactive Communication
Abbreviated titleRo-Man 2014
CountryUnited Kingdom
CityEdinburgh
Period25/08/1429/08/14

Fingerprint Dive into the research topics of 'Effect of cognitive biases on human-robot interaction: A case study of a robot's misattribution'. Together they form a unique fingerprint.

Cite this