Thorsten Bohnenberger, Saarland University
Human-computer interaction can sometimes be enhanced if a system can adapt to various aspects of the current situation (e.g., the user’s location, time constraints, cognitive load, and emotional state). It is sometimes useful to consider not only the immediate consequences of adaptation but also its (often uncertain) effects in the future. In the research summarized here, fully observable Markov decision processes have been successfully used to model and plan ahead the uncertain course of interaction for adaptation in two different scenarios. The approach needs to be generalized to deal with partial observability, so that a system cancope with situations in which not only the effects of its actions but also the current state of the interaction is uncertain.