This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

Using anthropological theory, this paper examines human–android interactions (HAI) as an emerging aspect of android science. These interactions are described in terms of adaptive learning (which is largely subconscious). This article is based on the observations reported and supplementary data from two studies that took place in Japan with a teleoperated android robot called Telenoid in the socialization of school children and older adults. We argue that interacting with androids brings about a special context, an interval, and a space/time for (...) reflection and imagination that was not there before. During the interaction something happens. There is adaptive learning and as a result, both children and older adults accepted Telenoid, and the children and older adults accepted each other. Using frames of play and ritual, we make sense and ‘capture’ moments of adaptive learning, and the feedback that elicits a social response from all study participants that results in self-efficacy and socialization. While “ritual” refers to the application of what has been learned and “play” means that there are no obvious consequences of what has been learned. This analysis illuminates new understanding about the uncanny valley, cultural robotics and the therapeutic potential of HAI. This has implications for the acceptance of androids in ‘socialized roles’ and gives us insight into the subconscious adaptive learning processes that must take place within humans to accept androids into our society. This approach aims to provides a clearer conceptual basis and vocabulary for further research of android and humanoid development. (shrink)