Modeling user intentions for in-car infotainment systems using Bayesian networks

Abstract

To support users in operating a computer system with a varying set of functions, it is fundamental to understand their intentions, e.g., within an in-car infotainment system. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling user intentions is currently not supported. However, manually creating software that predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user's intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible intentions of users according to the current situation. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user's behavior. We demonstrate feasibility of our approach using an industrial example of an intention-aware in-car infotainment system.