Effects of agendas on model-based intention inference of cooperative teams

Abstract

Ubiquitous computing aims for the realization of environments that assist users autonomously and proactively. Therefore smart environment infrastructures need to be able to identify users needs (intention recognition) and to plan an appropriate assisting strategy. Both is matter for research. In our approach we address inferring the intention of a team within a smart meeting environment. This becomes a central challenge, especially if multiple users are observed by noisy heterogeneous sensors. We propose a team behavior model based on hierarchical dynamic Bayesian network (DBN) for inferring the current task and activity of a team of users online. Given (noisy and intermittent) sensor readings of the team members' positions in a meeting room, we are interested in inferring the team's current objective. We implemented the model using particle filters for inference and demonstrate that by adding knowledge about the meeting agenda prediction accuracy and speed is improved. Evaluation of simulation data answers the question, how precise agenda knowledge must be to predict team behavior optimally.