Embodied conversational agents still do not achieve the fluidity and smoothness of natural conversational interaction. One main reason is that current system often respond with big latencies and in inflexible ways. We argue that to overcome these problems, real-time conversational agents need to be based on an underlying architecture that provides two essential features for fast and fluent behavior adaptation: A close bi-directional coordination between input processing and output generation, and incrementality of processing at both stages. We propose an architectural framework for conversational agents (ASAP) providing these two ingredients for fluid real-time conversation. The overall architectural concept is described, along with specific means of specifying incremental behavior in BML and technical implementations of different modules. We show how phenomena of fluid realtime conversation, like adapting to user feedback or smooth turn-keeping, can be realized with ASAP and we describe in detail an example real-time interaction with the implemented system.

The recent surge of interest in explainability in artificial intelligence (XAI) is propelled by not only technological advancements in machine learning, but also by regulatory initiatives to foster transparency in algorithmic decision making. In this article, we revise the current concept of explainability and identify three limitations: passive explainee, narrow view on the social process, and undifferentiated assessment of understanding. In order to overcome these limitations, we present explanation as a social practice in which explainer and explainee co-construct understanding on the microlevel. We view the co-construction on a microlevel as embedded into a macrolevel, yielding expectations concerning, e.g., social roles or partner models: Typically, the role of the explainer is to provide an explanation and to adapt it to the current level of understanding of the explainee; the explainee, in turn, is expected to provide cues that guide the explainer. Building on explanations being a social practice, we present a conceptual framework that aims to guide future research in XAI. The framework relies on the key concepts of monitoring and scaffolding to capture the development of interaction. We relate our conceptual framework and our new perspective on explaining to transparency and autonomy as objectives considered for XAI.

Successful dialogue is based on collaborative efforts of the interactants to ensure mutual understanding. This paper presents work towards making conversational agents 'attentive speakers' that continuously attend to the communicative feedback given by their interlocutors and adapt their ongoing and subsequent communicative behaviour to their needs. A comprehensive conceptual and architectural model for this is proposed and first steps of its realisation are described. Results from a prototype implementation are presented.

The ALICO corpus: analysing the active listener. Abstract The Active Listening Corpus (ALICO) is a multimodal data set of spontaneous dyadic conversations in German with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcriptions with corresponding communicative function interpretations as well as segmentations of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotations of both interlocutors. The corpus contributes to research on spontaneous human-human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and briefly present the most important results obtained from analyses in recent years.

Holding non-co-located conversations while driving is dangerous (Horrey and Wickens, 2006;Strayer et al., 2006), much more so than conversations with physically present, "situated" interlocutors (Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected, and resume when over. We show that this improves both driving performance and recall of system-presented information, compared to a non-adaptive strategy.