Improving proactive dialog agents using socially-aware reinforcement learning

  • The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successfulThe next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.show moreshow less

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Metadaten
Author:Matthias KrausORCiDGND, Nicolas Wagner, Ron Riekenbrauck, Wolfgang Minker
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/101335
ISBN:978-1-4503-9932-6OPAC
Parent Title (English):UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, Limassol, Cyprus, June 26-29, 2023
Publisher:Association for Computing Machinery (ACM)
Place of publication:New York, NY
Type:Conference Proceeding
Language:English
Year of first Publication:2023
Release Date:2023/01/30
First Page:146
Last Page:155
DOI:https://doi.org/10.1145/3565472.3595611
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik