Using Autoepistemic Logics for Understandable and Flexible User-Models

Johanna Wolff; Victor De Boer; Dirk Heylen; M Birna Van Riemsdijk

Using Autoepistemic Logics for Understandable and Flexible User-Models

Johanna Wolff^*, Victor De Boer, Dirk Heylen, M Birna Van Riemsdijk

^*Corresponding author for this work

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

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Abstract

Behavior change support agents are most effective when they are personalized to the user's goals and motivations. To achieve this the agent should be able to create a user model based on limited initial inputs from the user. We demonstrate how autoepistemic logic can be used to build a model which combines direct input from the user with assumptions about the user's reasoning. These beliefs can be used when reasoning about the user's motivations, but they may also be rejected when presented with conflicting information. This results in a user model in which both knowledge and beliefs about the user are included but still clearly separated. We illustrate our ideas using an example of a behavior support agent which assists the user in exercising more.

Original language	English
Title of host publication	Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23)
Publisher	CEUR
Pages	133-136
Number of pages	4
Publication status	Published - 2023
Event	21st International Workshop on Nonmonotonic Reasoning, NMR 2023 - Rhodes, Greece Duration: 2 Sept 2023 → 4 Sept 2023 Conference number: 21

Publication series

Name	CEUR Workshop proceedings
Publisher	CEUR
Volume	3464

Conference

Conference	21st International Workshop on Nonmonotonic Reasoning, NMR 2023
Abbreviated title	NMR 2023
Country/Territory	Greece
City	Rhodes
Period	2/09/23 → 4/09/23

Keywords

Autoepistemic logic
Behavior support agent
Shared mental models
User-Model

Access to Document

ArticleFinal published version, 232 KBLicence: CC BY

https://ceur-ws.org/Vol-3464/Licence: CC BY

Cite this

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title = "Using Autoepistemic Logics for Understandable and Flexible User-Models",

abstract = "Behavior change support agents are most effective when they are personalized to the user's goals and motivations. To achieve this the agent should be able to create a user model based on limited initial inputs from the user. We demonstrate how autoepistemic logic can be used to build a model which combines direct input from the user with assumptions about the user's reasoning. These beliefs can be used when reasoning about the user's motivations, but they may also be rejected when presented with conflicting information. This results in a user model in which both knowledge and beliefs about the user are included but still clearly separated. We illustrate our ideas using an example of a behavior support agent which assists the user in exercising more.",

keywords = "Autoepistemic logic, Behavior support agent, Shared mental models, User-Model",

author = "Johanna Wolff and Boer, {Victor De} and Dirk Heylen and Riemsdijk, {M Birna Van}",

note = "Publisher Copyright: {\textcopyright} 2022 Copyright for this paper by its authors.; 21st International Workshop on Nonmonotonic Reasoning, NMR 2023, NMR 2023 ; Conference date: 02-09-2023 Through 04-09-2023",

year = "2023",

language = "English",

series = "CEUR Workshop proceedings",

publisher = "CEUR",

pages = "133--136",

booktitle = "Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23)",

}

Wolff, J, Boer, VD, Heylen, D & Riemsdijk, MBV 2023, Using Autoepistemic Logics for Understandable and Flexible User-Models. in Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23). CEUR Workshop proceedings, vol. 3464, CEUR, pp. 133-136, 21st International Workshop on Nonmonotonic Reasoning, NMR 2023, Rhodes, Greece, 2/09/23. <https://ceur-ws.org/Vol-3464/>

Using Autoepistemic Logics for Understandable and Flexible User-Models. / Wolff, Johanna; Boer, Victor De; Heylen, Dirk et al.
Proceedings of the 21st International Workshop on Non-Monotonic Reasoning (NMR'23). CEUR, 2023. p. 133-136 (CEUR Workshop proceedings; Vol. 3464).

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

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AB - Behavior change support agents are most effective when they are personalized to the user's goals and motivations. To achieve this the agent should be able to create a user model based on limited initial inputs from the user. We demonstrate how autoepistemic logic can be used to build a model which combines direct input from the user with assumptions about the user's reasoning. These beliefs can be used when reasoning about the user's motivations, but they may also be rejected when presented with conflicting information. This results in a user model in which both knowledge and beliefs about the user are included but still clearly separated. We illustrate our ideas using an example of a behavior support agent which assists the user in exercising more.

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Using Autoepistemic Logics for Understandable and Flexible User-Models

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21st International Workshop on Nonmonotonic Reasoning, NMR 2023

Cite this

Using Autoepistemic Logics for Understandable and Flexible User-Models

Abstract

Publication series

Conference

Keywords

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21st International Workshop on Nonmonotonic Reasoning, NMR 2023

Cite this