---
_id: '51342'
abstract:
- lang: eng
text: Intelligent agents interacting with humans through conversation (such as a
robot, embodied conversational agent, or chatbot) need to receive feedback from
the human to make sure that its communicative acts have the intended consequences.
At the same time, the human interacting with the agent will also seek feedback,
in order to ensure that her communicative acts have the intended consequences.
In this review article, we give an overview of past and current research on how
intelligent agents should be able to both give meaningful feedback toward humans,
as well as understanding feedback given by the users. The review covers feedback
across different modalities (e.g., speech, head gestures, gaze, and facial expression),
different forms of feedback (e.g., backchannels, clarification requests), and
models for allowing the agent to assess the user's level of understanding and
adapt its behavior accordingly. Finally, we analyse some shortcomings of current
approaches to modeling feedback, and identify important directions for future
research.
author:
- first_name: Agnes
full_name: Axelsson, Agnes
last_name: Axelsson
- first_name: Hendrik
full_name: Buschmeier, Hendrik
id: '76456'
last_name: Buschmeier
orcid: 0000-0002-9613-5713
- first_name: Gabriel
full_name: Skantze, Gabriel
last_name: Skantze
citation:
ama: Axelsson A, Buschmeier H, Skantze G. Modeling Feedback in Interaction With
Conversational Agents—A Review. Frontiers in Computer Science. 2022;4.
doi:10.3389/fcomp.2022.744574
apa: Axelsson, A., Buschmeier, H., & Skantze, G. (2022). Modeling Feedback in
Interaction With Conversational Agents—A Review. Frontiers in Computer Science,
4. https://doi.org/10.3389/fcomp.2022.744574
bibtex: '@article{Axelsson_Buschmeier_Skantze_2022, title={Modeling Feedback in
Interaction With Conversational Agents—A Review}, volume={4}, DOI={10.3389/fcomp.2022.744574},
journal={Frontiers in Computer Science}, publisher={Frontiers Media SA}, author={Axelsson,
Agnes and Buschmeier, Hendrik and Skantze, Gabriel}, year={2022} }'
chicago: Axelsson, Agnes, Hendrik Buschmeier, and Gabriel Skantze. “Modeling Feedback
in Interaction With Conversational Agents—A Review.” Frontiers in Computer
Science 4 (2022). https://doi.org/10.3389/fcomp.2022.744574.
ieee: 'A. Axelsson, H. Buschmeier, and G. Skantze, “Modeling Feedback in Interaction
With Conversational Agents—A Review,” Frontiers in Computer Science, vol.
4, 2022, doi: 10.3389/fcomp.2022.744574.'
mla: Axelsson, Agnes, et al. “Modeling Feedback in Interaction With Conversational
Agents—A Review.” Frontiers in Computer Science, vol. 4, Frontiers Media
SA, 2022, doi:10.3389/fcomp.2022.744574.
short: A. Axelsson, H. Buschmeier, G. Skantze, Frontiers in Computer Science 4 (2022).
date_created: 2024-02-14T08:46:21Z
date_updated: 2025-09-11T10:17:11Z
doi: 10.3389/fcomp.2022.744574
extern: '1'
intvolume: ' 4'
keyword:
- General Medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
project:
- _id: '112'
name: 'TRR 318 - A02: TRR 318 - Verstehensprozess einer Erklärung beobachten und
auswerten (Teilprojekt A02)'
publication: Frontiers in Computer Science
publication_identifier:
issn:
- 2624-9898
publication_status: published
publisher: Frontiers Media SA
quality_controlled: '1'
status: public
title: Modeling Feedback in Interaction With Conversational Agents—A Review
type: journal_article
user_id: '76456'
volume: 4
year: '2022'
...
---
_id: '23526'
abstract:
- lang: eng
text: Modern and flexible application-level software platforms increase
the attack surface of connected vehicles and thereby require automotive engineers
to adopt additional security control techniques. These techniques encompass host-based
intrusion detection systems (HIDSs) that detect suspicious activities in application
contexts. Such application-aware HIDSs originate in information and communications
technology systems and have a great potential to deal with the flexible nature
of application-level software platforms. However, the elementary characteristics
of known application-aware HIDS approaches and thereby the implications for their
transfer to the automotive sector are unclear. In previous work, we presented
a systematic literature review (SLR) covering the state of the art of application-aware
HIDS approaches. We synthesized our findings by means of a fine-grained classification
for each approach specified through a feature model and corresponding variant
models. These models represent the approaches’ elementary characteristics. Furthermore,
we summarized key findings and inferred implications for the transfer of application-aware
HIDSs to the automotive sector. In this article, we extend the previous work by
several aspects. We adjust the quality evaluation process within the SLR to be
able to consider high quality conference publications, which results in an extended
final pool of publications. For supporting HIDS developers on the task of configuring
HIDS analysis techniques based on machine learning, we report on initial results
on the applicability of AutoML. Furthermore, we present lessons learned regarding
the application of the feature and variant model approach for SLRs. Finally, we
more thoroughly describe the SLR study design.
author:
- first_name: David
full_name: Schubert, David
id: '9106'
last_name: Schubert
- first_name: Hendrik
full_name: Eikerling, Hendrik
id: '29279'
last_name: Eikerling
- first_name: Jörg
full_name: Holtmann, Jörg
id: '3875'
last_name: Holtmann
orcid: 0000-0001-6141-4571
citation:
ama: 'Schubert D, Eikerling H, Holtmann J. Application-Aware Intrusion Detection:
A Systematic Literature Review, Implications for Automotive Systems, and Applicability
of AutoML. Frontiers in Computer Science. 2021;3. doi:10.3389/fcomp.2021.567873'
apa: 'Schubert, D., Eikerling, H., & Holtmann, J. (2021). Application-Aware
Intrusion Detection: A Systematic Literature Review, Implications for Automotive
Systems, and Applicability of AutoML. Frontiers in Computer Science, 3.
https://doi.org/10.3389/fcomp.2021.567873'
bibtex: '@article{Schubert_Eikerling_Holtmann_2021, title={Application-Aware Intrusion
Detection: A Systematic Literature Review, Implications for Automotive Systems,
and Applicability of AutoML}, volume={3}, DOI={10.3389/fcomp.2021.567873},
journal={Frontiers in Computer Science}, publisher={Frontiers Media}, author={Schubert,
David and Eikerling, Hendrik and Holtmann, Jörg}, year={2021} }'
chicago: 'Schubert, David, Hendrik Eikerling, and Jörg Holtmann. “Application-Aware
Intrusion Detection: A Systematic Literature Review, Implications for Automotive
Systems, and Applicability of AutoML.” Frontiers in Computer Science 3
(2021). https://doi.org/10.3389/fcomp.2021.567873.'
ieee: 'D. Schubert, H. Eikerling, and J. Holtmann, “Application-Aware Intrusion
Detection: A Systematic Literature Review, Implications for Automotive Systems,
and Applicability of AutoML,” Frontiers in Computer Science, vol. 3, 2021.'
mla: 'Schubert, David, et al. “Application-Aware Intrusion Detection: A Systematic
Literature Review, Implications for Automotive Systems, and Applicability of AutoML.”
Frontiers in Computer Science, vol. 3, Frontiers Media, 2021, doi:10.3389/fcomp.2021.567873.'
short: D. Schubert, H. Eikerling, J. Holtmann, Frontiers in Computer Science 3 (2021).
date_created: 2021-08-26T09:53:54Z
date_updated: 2022-01-06T06:55:56Z
department:
- _id: '241'
- _id: '662'
doi: 10.3389/fcomp.2021.567873
intvolume: ' 3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.frontiersin.org/articles/10.3389/fcomp.2021.567873/full
oa: '1'
publication: Frontiers in Computer Science
publication_identifier:
issn:
- 2624-9898
publication_status: published
publisher: Frontiers Media
status: public
title: 'Application-Aware Intrusion Detection: A Systematic Literature Review, Implications
for Automotive Systems, and Applicability of AutoML'
type: journal_article
user_id: '29279'
volume: 3
year: '2021'
...