TY - JOUR
T1 - A safe collaborative chatbot for smart home assistants
AU - Chkroun, Merav
AU - Azaria, Amos
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Smart home assistants, which enable users to control home appliances and can be used for holding entertaining conversations, have become an inseparable part of many people’s homes. Recently, there have been many attempts to allow end-users to teach a home assistant new commands, responses, and rules, which can then be shared with a larger community. However, allowing end-users to teach an agent new responses, which are shared with a large community, opens the gate to malicious users, who can teach the agent inappropriate responses in order to promote their own business, products, or political views. In this paper, we present a platform that enables users to collaboratively teach a smart home assistant (or chatbot) responses using natural language. We present a method of collectively detecting malicious users and using the commands taught by the malicious users to further mitigate activity of future malicious users. We ran an experiment with 192 subjects and show the effectiveness of our platform.
AB - Smart home assistants, which enable users to control home appliances and can be used for holding entertaining conversations, have become an inseparable part of many people’s homes. Recently, there have been many attempts to allow end-users to teach a home assistant new commands, responses, and rules, which can then be shared with a larger community. However, allowing end-users to teach an agent new responses, which are shared with a large community, opens the gate to malicious users, who can teach the agent inappropriate responses in order to promote their own business, products, or political views. In this paper, we present a platform that enables users to collaboratively teach a smart home assistant (or chatbot) responses using natural language. We present a method of collectively detecting malicious users and using the commands taught by the malicious users to further mitigate activity of future malicious users. We ran an experiment with 192 subjects and show the effectiveness of our platform.
KW - Collaborative smart home assistants
KW - Human–agent interaction
KW - Mitigating offensive behavior
KW - Smart environments
UR - http://www.scopus.com/inward/record.url?scp=85116374007&partnerID=8YFLogxK
U2 - 10.3390/s21196641
DO - 10.3390/s21196641
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C2 - 34640960
AN - SCOPUS:85116374007
SN - 1424-8220
VL - 21
JO - Sensors
JF - Sensors
IS - 19
M1 - 6641
ER -