Sit, Eat, Drink, Talk, Laugh – Dining and Mixed Media, is an exploratory study of qualities in everyday life and challenges people to enjoy the qualities of mundanity. Seeking inspiration in ethnographic studies, field work was conducted in domestic settings, returning an extensive body of material to work from. The study challenges people to absorb the moment, reflect and enjoy, rather than pacing through a lifetime, with a constant focus on the future instead of the present. This work takes a starting point in food and dining as a social activity, where interactive sound and a reference to online social media is explored through two interventions. The results of these are discussed with central findings around food and dining in the area of sociology, the use of sound in ambient computing and on a higher level around the topic of temporality.

Comme dans de nombreux autres domaines, la construction des applications en Informatique Ambiantes (IAm) se fait par réutilisation d’entités logicielles disponibles. Pour des raisons de conductivités, de pannes, de charge de batterie mais aussi de nombreuses autres, la disponibilité de ces entités est imprévisible ce qui implique que l’auto-adaptation dynamique des applications est une nécessité. Cela passe par la spécification en parallèle des adaptations par des experts de divers domaines. Ce parallélisme de construction, peut amener des problèmes d’interférences lors de la composition dynamique de plusieurs adaptations. Dans cette thèse, par l’utilisation de graphes, nous contribuons à la définition d’un cadre formel pour la détection et la résolution de ces interférences. L’assemblage des entités logicielles repose sur des connecteurs d’assemblage qui sont utilisés dans la spécification des adaptations. Des règles de réécriture de graphe permettront de résoudre les interférences détectées, cette résolution étant guidée par la connaissance de connecteurs définis. De plus, pour pouvoir étendre dynamiquement et automatiquement notre mécanisme de gestion des interférences, nous proposons la modélisation comportementale de ces connecteurs. Ceci permet de ne pas reposer sur une connaissance à priori des connecteurs et autorise par la même d’étendre dynamiquement l’ensemble des connecteurs disponibles pour la spécification des adaptations. / Like many other fields, application construction in ubiquitous computing is done by reuse of available software entities. For reasons of conductivity, breakdown, battery charge, but also many others reasons, the availability of these entities is unpredictable. As consequence, the self-adaptation of applications becomes necessary. This requires the specification of parallel adaptations by experts from various fields. This parallel specification can cause interference problems when several adaptations are composed. In this thesis, using graph formalism, we contribute to the definition of a formal approach for the detection and the resolution of interferences. The specification of adaptation uses connectors in order to assemble software entities. Graph rewriting rules are defined to solve the detected interferences. This resolution is guided by the knowledge of defined connectors. In addition, in order to extend dynamically and automatically our interference management mechanism, we propose behavioral modeling of these connectors. This allows us extending our mechanism without an a priori knowledge of connectors and allows afterwards to extend the set of available connectors used for adaptations' specifications.

Even though most of our life happens in the physical world, most applications of AI are digital. We have to explain our intention and context to chatbots for them to support us with things happening in the world around us. Archetype AI, the collaboration partner of this thesis, is developing a Large Behavior Model (LBM) that perceivesand reasons about the physical world in real time by fusing multimodal sensor data and natural language. This thesis is a side track to the technical development of this LBM, exploring the landscape of interactions enabled when AI understands the world around us. A series of interventions with physical AI as a design material is presented to illustrate what the relation with such intelligent systems could look like in the future. The research also highlights the evolving role of designers, which transitions from crafting explicit interactions to shaping the behaviour of intelligent and context-aware environments. Lastly, the research raises questions to start asking as AI, and its role in our lives is evolving.