1,067 research outputs found
Understanding How Well You Understood – Context-sensitive Interpretation of Multimodal User Feedback
Full text linkBuschmeier H, Kopp S. Understanding How Well You Understood – Context-sensitive Interpretation of Multimodal User Feedback. In: Proceedings of the 12th International Conference on Intelligent Virtual Agents. Santa Cruz, CA; 2012: 517-519
Unveiling the Information State with a Bayesian Model of the Listener
Full text linkBuschmeier H, Kopp S. Unveiling the Information State with a Bayesian Model of the Listener. In: SemDial 2011: Proceedings of the 15th Workshop on the Semantics and Pragmatics of Dialogue. 2011: 178-179.Attentive speaker agents – artificial conversational agents that can attend to and adapt to listener feedback – need to attribute a mental ‘listener state’ to the user and keep track of the grounding status of their own utterances. We propose a joint model of listener state and information state, represented as a dynamic Bayesian network, that can capture the influences between dialogue context, user feedback, the mental listener state and the information state, providing an estimation of grounding
Supplementary material RO-MAN 2023
No full textSupplementary material to the paper ‘Indirect Politeness of Disconfirming Answers to Humans and Robots’ (Ro-MAN 2023) by Eleonore Lumer, Clara Lachenmaier, Sina Zarrieß, Hendrik Buschmeier
Supplementary Material to "Desirability of Proactive Robots: A User Study on Spoken Interaction Initiation"
No full textSupplementary Material to the publication Anargh Viswanath and Hendrik Buschmeier. 2026. Desirability of Proactive Robots: A User Study on Spoken Interaction Initiation. In Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI ’26), March 16–19, 2026, Edinburgh, Scotland, UK. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3757279.378555
Using a Bayesian model of the listener to unveil the dialogue information state
Full text linkBuschmeier H, Kopp S. Using a Bayesian model of the listener to unveil the dialogue information state. In: SemDial 2012: Proceedings of the 16th Workshop on the Semantics and Pragmatics of Dialogue. 2012: 12-20.Communicative listener feedback is a prevalent coordination mechanism in dialogue. Listeners use feedback to provide evidence of understanding to speakers, who, in turn, use it to reason about the listeners' mental state of listening, determine the groundedness of communicated information, and adapt their subsequent utterances to the listeners' needs. We describe a speaker-centric Bayesian model of listeners and their feedback behaviour, which can interpret the listener's feedback signal in its dialogue context and reason about the listener's mental state as well as the grounding status of objects in information state
How Should Attentive Speaker Agents Adapt to Listener Feedback?
Full text linkBuschmeier H, Kopp S. How Should Attentive Speaker Agents Adapt to Listener Feedback? In: Proceedings of The Listening Talker. An Interdisciplinary Workshop on Natural and Synthetic Modification of Speech in Response to Listening Conditions. Edinburgh, UK; 2012: 56
Supplementary Material: Can AI explain AI? Interactive co-construction of explanations among human and artificial agents
No full textSupplementary material to: Klowait, Nils, Maria Erofeeva, Michael Lenke, Ilona Horwath & Hendrik Buschmeier. 2024. Can AI explain AI? Interactive co-construction of explanations among human and artificial agents. Discourse & Communication 18(6). https://doi.org/10.1177/17504813241267069
Adapting Language Production to Listener Feedback Behaviour
Full text linkBuschmeier H, Kopp S. Adapting Language Production to Listener Feedback Behaviour. In: Proceedings of the Interdisciplinary Workshop on Feedback Behaviors in Dialogue. Stevenson, WA; 2012: 7-10.Listeners use linguistic feedback to provide evidence of understanding to speakers. They, in turn, use it to reason about listeners' mental states, to determine the groundedness of communicated information and to adapt subsequent utterances to the listeners' needs. We describe a probabilistic model for the interpretation of listener feedback in its dialogue context that enables a speaker to evaluate the listener's mental state and gauge common ground. We then discuss levels and mechanisms of adaptation that speaker's commonly use in reaction to listener feedback
Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent Dialogue
Full text linkBuschmeier H, Kopp S. Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent Dialogue. KI - Künstliche Intelligenz. 2013;27(2):137-143.Grounding in dialogue concerns the question of how the gap between the individual symbol systems of interlocutors can be bridged so that mutual understanding is possible. This problem is highly relevant to human–agent interaction where mis- or non-understanding is common. We argue that humans minimise this gap by collaboratively and iteratively creating a shared conceptualisation that serves as a basis for negotiating symbol meaning. We then present a computational model that enables an artificial conversational agent to estimate the user's mental state (in terms of contact, perception, understanding, acceptance, agreement and based upon his or her feedback signals) and use this information to incrementally adapt its ongoing communicative actions to the user's needs. These basic abilities are important to reduce friction in the iterative coordination process of co-constructing grounded symbols in dialogue
Supplementary material HRI2022-LBR
No full textSupplementary Material to Lumer, E., & Buschmeier, H. (2022). Perception of power and distance in human-human and human-robot role-based relations. In: Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction – Late Breaking Reports
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