[{"citation":{"ama":"Axelsson A, Buschmeier H, Skantze G. Modeling Feedback in Interaction With Conversational Agents—A Review. <i>Frontiers in Computer Science</i>. 2022;4. doi:<a href=\"https://doi.org/10.3389/fcomp.2022.744574\">10.3389/fcomp.2022.744574</a>","bibtex":"@article{Axelsson_Buschmeier_Skantze_2022, title={Modeling Feedback in Interaction With Conversational Agents—A Review}, volume={4}, DOI={<a href=\"https://doi.org/10.3389/fcomp.2022.744574\">10.3389/fcomp.2022.744574</a>}, journal={Frontiers in Computer Science}, publisher={Frontiers Media SA}, author={Axelsson, Agnes and Buschmeier, Hendrik and Skantze, Gabriel}, year={2022} }","mla":"Axelsson, Agnes, et al. “Modeling Feedback in Interaction With Conversational Agents—A Review.” <i>Frontiers in Computer Science</i>, vol. 4, Frontiers Media SA, 2022, doi:<a href=\"https://doi.org/10.3389/fcomp.2022.744574\">10.3389/fcomp.2022.744574</a>.","short":"A. Axelsson, H. Buschmeier, G. Skantze, Frontiers in Computer Science 4 (2022).","chicago":"Axelsson, Agnes, Hendrik Buschmeier, and Gabriel Skantze. “Modeling Feedback in Interaction With Conversational Agents—A Review.” <i>Frontiers in Computer Science</i> 4 (2022). <a href=\"https://doi.org/10.3389/fcomp.2022.744574\">https://doi.org/10.3389/fcomp.2022.744574</a>.","apa":"Axelsson, A., Buschmeier, H., &#38; Skantze, G. (2022). Modeling Feedback in Interaction With Conversational Agents—A Review. <i>Frontiers in Computer Science</i>, <i>4</i>. <a href=\"https://doi.org/10.3389/fcomp.2022.744574\">https://doi.org/10.3389/fcomp.2022.744574</a>","ieee":"A. Axelsson, H. Buschmeier, and G. Skantze, “Modeling Feedback in Interaction With Conversational Agents—A Review,” <i>Frontiers in Computer Science</i>, vol. 4, 2022, doi: <a href=\"https://doi.org/10.3389/fcomp.2022.744574\">10.3389/fcomp.2022.744574</a>."},"quality_controlled":"1","project":[{"_id":"112","name":"TRR 318 - A02: TRR 318 - Verstehensprozess einer Erklärung beobachten und auswerten (Teilprojekt A02)"}],"oa":"1","status":"public","publisher":"Frontiers Media SA","_id":"51342","user_id":"76456","volume":4,"publication":"Frontiers in Computer Science","abstract":[{"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.","lang":"eng"}],"extern":"1","date_created":"2024-02-14T08:46:21Z","type":"journal_article","keyword":["General Medicine"],"title":"Modeling Feedback in Interaction With Conversational Agents—A Review","year":"2022","publication_identifier":{"issn":["2624-9898"]},"author":[{"full_name":"Axelsson, Agnes","last_name":"Axelsson","first_name":"Agnes"},{"full_name":"Buschmeier, Hendrik","orcid":"0000-0002-9613-5713","first_name":"Hendrik","last_name":"Buschmeier","id":"76456"},{"first_name":"Gabriel","last_name":"Skantze","full_name":"Skantze, Gabriel"}],"date_updated":"2025-09-11T10:17:11Z","publication_status":"published","intvolume":"         4","main_file_link":[{"open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.3389/fcomp.2022.744574"},{"status":"public","volume":3,"user_id":"29279","publisher":"Frontiers Media","_id":"23526","citation":{"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={<a href=\"https://doi.org/10.3389/fcomp.2021.567873\">10.3389/fcomp.2021.567873</a>}, journal={Frontiers in Computer Science}, publisher={Frontiers Media}, author={Schubert, David and Eikerling, Hendrik and Holtmann, Jörg}, year={2021} }","ama":"Schubert D, Eikerling H, Holtmann J. Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML. <i>Frontiers in Computer Science</i>. 2021;3. doi:<a href=\"https://doi.org/10.3389/fcomp.2021.567873\">10.3389/fcomp.2021.567873</a>","mla":"Schubert, David, et al. “Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML.” <i>Frontiers in Computer Science</i>, vol. 3, Frontiers Media, 2021, doi:<a href=\"https://doi.org/10.3389/fcomp.2021.567873\">10.3389/fcomp.2021.567873</a>.","short":"D. Schubert, H. Eikerling, J. Holtmann, Frontiers in Computer Science 3 (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.” <i>Frontiers in Computer Science</i> 3 (2021). <a href=\"https://doi.org/10.3389/fcomp.2021.567873\">https://doi.org/10.3389/fcomp.2021.567873</a>.","ieee":"D. Schubert, H. Eikerling, and J. Holtmann, “Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML,” <i>Frontiers in Computer Science</i>, vol. 3, 2021.","apa":"Schubert, D., Eikerling, H., &#38; Holtmann, J. (2021). Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML. <i>Frontiers in Computer Science</i>, <i>3</i>. <a href=\"https://doi.org/10.3389/fcomp.2021.567873\">https://doi.org/10.3389/fcomp.2021.567873</a>"},"oa":"1","intvolume":"         3","date_updated":"2022-01-06T06:55:56Z","publication_status":"published","publication_identifier":{"issn":["2624-9898"]},"author":[{"last_name":"Schubert","first_name":"David","full_name":"Schubert, David","id":"9106"},{"first_name":"Hendrik","last_name":"Eikerling","full_name":"Eikerling, Hendrik","id":"29279"},{"last_name":"Holtmann","first_name":"Jörg","orcid":"0000-0001-6141-4571","full_name":"Holtmann, Jörg","id":"3875"}],"year":"2021","title":"Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML","doi":"10.3389/fcomp.2021.567873","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://www.frontiersin.org/articles/10.3389/fcomp.2021.567873/full","open_access":"1"}],"abstract":[{"lang":"eng","text":"<jats:p>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.</jats:p>"}],"publication":"Frontiers in Computer Science","department":[{"_id":"241"},{"_id":"662"}],"type":"journal_article","date_created":"2021-08-26T09:53:54Z"}]