‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking

Raj Bhalwankar; Jan Treur

doi:10.1007/978-3-030-79150-6_1

‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking

Raj Bhalwankar, Jan Treur^*

^*Corresponding author for this work

Computer Science

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

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Abstract

In this paper counterfactual thinking is addressed based on literature mainly from Neuroscience and Psychology. A detailed literature review was conducted in identifying processes, neural correlates and theories related to counterfactual thinking from different disciplines. A familiar scenario with respect to counterfactual thinking was identified. Based on the literature, an adaptive self-modeling network model was designed. This model captures the complex process of counterfactual thinking and the learning and control involved.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations
Subtitle of host publication	17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings
Editors	Ilias Maglogiannis, John Macintyre, Lazaros Iliadis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-16
Number of pages	14
ISBN (Electronic)	9783030791506
ISBN (Print)	9783030791490, 9783030791520
DOIs	https://doi.org/10.1007/978-3-030-79150-6_1
Publication status	Published - 2021
Event	17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 - Virtual, Online Duration: 25 Jun 2021 → 27 Jun 2021

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	627
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021
City	Virtual, Online
Period	25/06/21 → 27/06/21

Bibliographical note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

Adaptive network model
Control
Counterfactual thinking
Learning
Mental model

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10.1007/978-3-030-79150-6_1

Bhalwankar-Treur2021_Chapter_IfOnlyIWouldHaveDoneThatAControlledAdaptiveNetworkModelForFinal published version, 1.31 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/348324898

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Bhalwankar, R., & Treur, J. (2021). ‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking. In I. Maglogiannis, J. Macintyre, & L. Iliadis (Eds.), Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings (pp. 3-16). (IFIP Advances in Information and Communication Technology; Vol. 627). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-79150-6_1

Bhalwankar, Raj ; Treur, Jan. / ‘If Only I Would Have Done that…’ : A Controlled Adaptive Network Model for Learning by Counterfactual Thinking. Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings. editor / Ilias Maglogiannis ; John Macintyre ; Lazaros Iliadis. Springer Science and Business Media Deutschland GmbH, 2021. pp. 3-16 (IFIP Advances in Information and Communication Technology).

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author = "Raj Bhalwankar and Jan Treur",

note = "Publisher Copyright: {\textcopyright} 2021, IFIP International Federation for Information Processing. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 ; Conference date: 25-06-2021 Through 27-06-2021",

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doi = "10.1007/978-3-030-79150-6_1",

language = "English",

isbn = "9783030791490",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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Bhalwankar, R & Treur, J 2021, ‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking. in I Maglogiannis, J Macintyre & L Iliadis (eds), Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings. IFIP Advances in Information and Communication Technology, vol. 627, Springer Science and Business Media Deutschland GmbH, pp. 3-16, 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, Virtual, Online, 25/06/21. https://doi.org/10.1007/978-3-030-79150-6_1

‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking. / Bhalwankar, Raj; Treur, Jan.
Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings. ed. / Ilias Maglogiannis; John Macintyre; Lazaros Iliadis. Springer Science and Business Media Deutschland GmbH, 2021. p. 3-16 (IFIP Advances in Information and Communication Technology; Vol. 627).

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

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Bhalwankar R, Treur J. ‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking. In Maglogiannis I, Macintyre J, Iliadis L, editors, Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 3-16. (IFIP Advances in Information and Communication Technology). Epub 2021 Jun 22. doi: 10.1007/978-3-030-79150-6_1

‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking

Abstract

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