@inproceedings{2258a99c46a14daeb3e335997c828442,
title = "User-models to drive an adaptive virtual advisor",
abstract = "Agents that adapt to their user need to have knowledge of their user and expertise on how best to adapt to that type of user. In this paper we describe the addition of an agent's expertise and collection of machine-learnt user profiles to the proposed extended FAtiMA (Fearnot AffecTive Mind Architecture) cognitive agent architecture. A study to evaluate the extended architecture is presented which compares the benefit (i.e. reduced stress and increased rapport) of tailoring dialogue (i.e. empathic or neutral) to the specific user.",
keywords = "Agent's Expertise, User Model, Virtual Advisor, Virtual Humans",
author = "Hedieh Ranjbartabar and Deborah Richards and Bilgin, {Ayse Aysin} and Cat Kutay and Samuel Mascarenhas",
year = "2020",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "2117--2119",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020",
note = "19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 ; Conference date: 19-05-2020",
}