Predicting the Priority of Social Situations for Personal Assistant Agents

Ilir Kola, Myrthe Tielman, Catholijn M. Jonker, M. Birna van Riemsdijk

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Abstract

Personal assistant agents have been developed to help people in their daily lives with tasks such as agenda management. In order to provide better support, they should not only model the user’s internal aspects, but also their social situation. Current research on social context tackles this by modelling the social aspects of a situation from an objective perspective. In our approach, we model these social aspects of the situation from the user’s subjective perspective. We do so by using concepts from social science, and in turn apply machine learning techniques to predict the priority that the user would assign to these situations. Furthermore, we show that using these techniques allows agents to determine which features influenced these predictions. Results based on a crowd-sourcing user study suggest that our proposed model would enable personal assistant agents to differentiate between situations with high and low priority. We believe this to be a first step towards agents that better understand the user’s social situation, and adapt their support accordingly.

Original languageEnglish
Title of host publicationPRIMA 2020
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 23rd International Conference, 2020, Proceedings
EditorsTakahiro Uchiya, Quan Bai, Iván Marsá Maestre
PublisherSpringer
Pages231-247
Number of pages17
ISBN (Electronic)978-3-030-69322-0
ISBN (Print)978-3-030-69321-3
DOIs
Publication statusPublished - 2021
Event23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 - Virtual Online Conference
Duration: 18 Nov 202020 Nov 2020
Conference number: 23

Conference

Conference23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
Abbreviated titlePRIMA 2020
CityVirtual Online Conference
Period18/11/2020/11/20

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