User-models to drive an adaptive virtual advisor

Hedieh Ranjbartabar, Deborah Richards, Ayse Aysin Bilgin, Cat Kutay, Samuel Mascarenhas

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Place of PublicationBarcelona
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages2117-2119
Number of pages3
ISBN (Electronic)9781450375184
Publication statusPublished - 2020
Event19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand
Duration: 19 May 2020 → …

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
CountryNew Zealand
CityVirtual, Auckland
Period19/05/20 → …

Keywords

  • Agent's Expertise
  • User Model
  • Virtual Advisor
  • Virtual Humans

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