Towards realtime adaptation: uncovering user models from experimental data

Deborah Richards; Ayse Aysin Bilgin; Hedieh Ranjbartabar; Anupam Makhija

doi:10.1007/978-3-319-97289-3_4

Towards realtime adaptation: uncovering user models from experimental data

Deborah Richards^*, Ayse Aysin Bilgin, Hedieh Ranjbartabar, Anupam Makhija

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Virtual Worlds and the non-player characters that inhabit them often lack knowledge about their users. Users are treated as sources of input or feedback. At best, systems respond to the user’s behavioural data captured in logfiles. But there is no deep understanding of the player. Without this deep knowledge it is not possible for the computer to intelligently adapt. Relevant knowledge about the user will differ according to the application domain. Currently studies capture data such as biographical details, health status and history, psychological profiles, preferences and attitudes via questionnaires. This data can not be used in real time to influence the behaviour of the system. We suggest that data collected in past studies could be used to create user profiles and rules that can be used in real time for tailored interactions. We present two examples in this paper, one relating to an educational virtual world for science inquiry and the other involving the use of an Intelligent Virtual Agent to reduce study stress.

Original language	English
Title of host publication	Knowledge Management and Acquisition for Intelligent Systems
Subtitle of host publication	15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings
Editors	Kenichi Yoshida, Maria Lee
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	46-60
Number of pages	15
ISBN (Electronic)	9783319972893
ISBN (Print)	9783319972886
DOIs	https://doi.org/10.1007/978-3-319-97289-3_4
Publication status	Published - 1 Jan 2018
Event	15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018 held in conjunction with the 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 - Nanjing, China Duration: 28 Aug 2018 → 29 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11016 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018 held in conjunction with the 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018
Country/Territory	China
City	Nanjing
Period	28/08/18 → 29/08/18

Keywords

Data mining
Educational virtual worlds
Intelligent virtual agents
Knowledge acquisition

Access to Document

10.1007/978-3-319-97289-3_4

Cite this

Richards, D., Bilgin, A. A., Ranjbartabar, H., & Makhija, A. (2018). Towards realtime adaptation: uncovering user models from experimental data. In K. Yoshida, & M. Lee (Eds.), Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings (pp. 46-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11016 LNAI). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-97289-3_4

Richards, Deborah ; Bilgin, Ayse Aysin ; Ranjbartabar, Hedieh et al. / Towards realtime adaptation : uncovering user models from experimental data. Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings. editor / Kenichi Yoshida ; Maria Lee. Cham : Springer, Springer Nature, 2018. pp. 46-60 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Virtual Worlds and the non-player characters that inhabit them often lack knowledge about their users. Users are treated as sources of input or feedback. At best, systems respond to the user{\textquoteright}s behavioural data captured in logfiles. But there is no deep understanding of the player. Without this deep knowledge it is not possible for the computer to intelligently adapt. Relevant knowledge about the user will differ according to the application domain. Currently studies capture data such as biographical details, health status and history, psychological profiles, preferences and attitudes via questionnaires. This data can not be used in real time to influence the behaviour of the system. We suggest that data collected in past studies could be used to create user profiles and rules that can be used in real time for tailored interactions. We present two examples in this paper, one relating to an educational virtual world for science inquiry and the other involving the use of an Intelligent Virtual Agent to reduce study stress.",

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author = "Deborah Richards and Bilgin, {Ayse Aysin} and Hedieh Ranjbartabar and Anupam Makhija",

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Richards, D , Bilgin, AA, Ranjbartabar, H & Makhija, A 2018, Towards realtime adaptation: uncovering user models from experimental data. in K Yoshida & M Lee (eds), Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11016 LNAI, Springer, Springer Nature, Cham, pp. 46-60, 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018 held in conjunction with the 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, Nanjing, China, 28/08/18. https://doi.org/10.1007/978-3-319-97289-3_4

Towards realtime adaptation: uncovering user models from experimental data. / Richards, Deborah ; Bilgin, Ayse Aysin; Ranjbartabar, Hedieh et al.
Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings. ed. / Kenichi Yoshida; Maria Lee. Cham: Springer, Springer Nature, 2018. p. 46-60 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11016 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T2 - 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018 held in conjunction with the 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018

AU - Richards, Deborah

AU - Bilgin, Ayse Aysin

AU - Ranjbartabar, Hedieh

AU - Makhija, Anupam

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Virtual Worlds and the non-player characters that inhabit them often lack knowledge about their users. Users are treated as sources of input or feedback. At best, systems respond to the user’s behavioural data captured in logfiles. But there is no deep understanding of the player. Without this deep knowledge it is not possible for the computer to intelligently adapt. Relevant knowledge about the user will differ according to the application domain. Currently studies capture data such as biographical details, health status and history, psychological profiles, preferences and attitudes via questionnaires. This data can not be used in real time to influence the behaviour of the system. We suggest that data collected in past studies could be used to create user profiles and rules that can be used in real time for tailored interactions. We present two examples in this paper, one relating to an educational virtual world for science inquiry and the other involving the use of an Intelligent Virtual Agent to reduce study stress.

AB - Virtual Worlds and the non-player characters that inhabit them often lack knowledge about their users. Users are treated as sources of input or feedback. At best, systems respond to the user’s behavioural data captured in logfiles. But there is no deep understanding of the player. Without this deep knowledge it is not possible for the computer to intelligently adapt. Relevant knowledge about the user will differ according to the application domain. Currently studies capture data such as biographical details, health status and history, psychological profiles, preferences and attitudes via questionnaires. This data can not be used in real time to influence the behaviour of the system. We suggest that data collected in past studies could be used to create user profiles and rules that can be used in real time for tailored interactions. We present two examples in this paper, one relating to an educational virtual world for science inquiry and the other involving the use of an Intelligent Virtual Agent to reduce study stress.

KW - Data mining

KW - Educational virtual worlds

KW - Intelligent virtual agents

KW - Knowledge acquisition

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DO - 10.1007/978-3-319-97289-3_4

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Knowledge Management and Acquisition for Intelligent Systems

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A2 - Lee, Maria

PB - Springer, Springer Nature

CY - Cham

Y2 - 28 August 2018 through 29 August 2018

ER -

Richards D , Bilgin AA, Ranjbartabar H, Makhija A. Towards realtime adaptation: uncovering user models from experimental data. In Yoshida K, Lee M, editors, Knowledge Management and Acquisition for Intelligent Systems: 15th Pacific Rim Knowledge Acquisition Workshop, PKAW 2018, Proceedings. Cham: Springer, Springer Nature. 2018. p. 46-60. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-97289-3_4

Towards realtime adaptation: uncovering user models from experimental data

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