RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction

Dorota Glowacka; Andrew Howes; Jussi P. Jokinen; Antti Oulasvirta; Özgür Åzimsek

doi:10.1145/3411763.3441323

RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction

Dorota Glowacka, Andrew Howes, Jussi P. Jokinen, Antti Oulasvirta, Özgür Åzimsek

Computer Science

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

Abstract

Reinforcement learning (RL) is emerging as an approach to understand intelligence in both humans and machines. However, if RL is to have a meaningful impact in human-computer interaction, it is critical that these two threads are integrated. This is required for genuinely interactive RL-based systems which take into account user capacities and preferences. This workshop will build a community and form a research agenda for investigating RL in HCI.

Original language	English
Title of host publication	CHI EA '21
Subtitle of host publication	Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
Editors	Yoshifumi Kitamura, Aaron Quigley, Katherine Isbister, Takeo Igarashi
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781450380959
DOIs	https://doi.org/10.1145/3411763.3441323
Publication status	Published - 8 May 2021
Event	CHI '21: CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths - Virtual, Virtual, Online, Japan Duration: 8 May 2021 → 13 May 2021 https://chi2021.acm.org/

Publication series

Name	CHI: Conference on Human Factors in Computing Systems

Conference

Conference	CHI '21: CHI Conference on Human Factors in Computing Systems
Abbreviated title	CHI 2021
Country/Territory	Japan
City	Virtual, Online
Period	8/05/21 → 13/05/21
Internet address	https://chi2021.acm.org/

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

Keywords

applications
cognitive models
interative Artificial Intelligence
MDP
model-based/model free
POMDP
reinforcement learning

ASJC Scopus subject areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software

Access to Document

10.1145/3411763.3441323

Cite this

Glowacka, D., Howes, A., Jokinen, J. P., Oulasvirta, A., & Åzimsek, Ö. (2021). RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction. In Y. Kitamura, A. Quigley, K. Isbister, & T. Igarashi (Eds.), CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-3). Article 99 (CHI: Conference on Human Factors in Computing Systems). Association for Computing Machinery (ACM). https://doi.org/10.1145/3411763.3441323

Glowacka, Dorota ; Howes, Andrew ; Jokinen, Jussi P. et al. / RL4HCI : Reinforcement Learning for Humans, Computers, and Interaction. CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. editor / Yoshifumi Kitamura ; Aaron Quigley ; Katherine Isbister ; Takeo Igarashi. New York : Association for Computing Machinery (ACM), 2021. pp. 1-3 (CHI: Conference on Human Factors in Computing Systems).

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abstract = "Reinforcement learning (RL) is emerging as an approach to understand intelligence in both humans and machines. However, if RL is to have a meaningful impact in human-computer interaction, it is critical that these two threads are integrated. This is required for genuinely interactive RL-based systems which take into account user capacities and preferences. This workshop will build a community and form a research agenda for investigating RL in HCI.",

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author = "Dorota Glowacka and Andrew Howes and Jokinen, {Jussi P.} and Antti Oulasvirta and {\"O}zg{\"u}r {\AA}zimsek",

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doi = "10.1145/3411763.3441323",

language = "English",

series = "CHI: Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

pages = "1--3",

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Glowacka, D, Howes, A, Jokinen, JP, Oulasvirta, A & Åzimsek, Ö 2021, RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction. in Y Kitamura, A Quigley, K Isbister & T Igarashi (eds), CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems., 99, CHI: Conference on Human Factors in Computing Systems, Association for Computing Machinery (ACM), New York, pp. 1-3, CHI '21: CHI Conference on Human Factors in Computing Systems, Virtual, Online, Japan, 8/05/21. https://doi.org/10.1145/3411763.3441323

RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction. / Glowacka, Dorota; Howes, Andrew; Jokinen, Jussi P. et al.
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. ed. / Yoshifumi Kitamura; Aaron Quigley; Katherine Isbister; Takeo Igarashi. New York: Association for Computing Machinery (ACM), 2021. p. 1-3 99 (CHI: Conference on Human Factors in Computing Systems).

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

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AU - Åzimsek, Özgür

PY - 2021/5/8

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AB - Reinforcement learning (RL) is emerging as an approach to understand intelligence in both humans and machines. However, if RL is to have a meaningful impact in human-computer interaction, it is critical that these two threads are integrated. This is required for genuinely interactive RL-based systems which take into account user capacities and preferences. This workshop will build a community and form a research agenda for investigating RL in HCI.

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KW - cognitive models

KW - interative Artificial Intelligence

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PB - Association for Computing Machinery (ACM)

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Glowacka D, Howes A, Jokinen JP, Oulasvirta A, Åzimsek Ö. RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction. In Kitamura Y, Quigley A, Isbister K, Igarashi T, editors, CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery (ACM). 2021. p. 1-3. 99. (CHI: Conference on Human Factors in Computing Systems). doi: 10.1145/3411763.3441323

RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this