Unsupervised scoring for scalable Internet-based collaborative teleoperation

For applications in education and entertainment, scalable Internet-based collaborative teleoperation allows many users simultaneously to share control of a single device. Automated numerical methods that can assess and record performance provide an incentive for users to participate and a means to evaluate individual and group performance. In this paper we describe "unsupervised scoring": a numerical approach to assessment based on clustering and response time. Like unsupervised learning, this approach is based on identifying regularities in the input rather than comparing input with desired output specified by an external supervisor. We present an algorithm for rapidly computing user scores that scales linearly with the number of users. We describe an implemented Java-based user interface incorporating this metric, an application based on the classic Twister game, and results where individual scores are compared with group performance.