Virtual clones: Data-driven social navigation

Abstract

Millions of participants inhabit online virtual worlds such as SL and engage in a wide range of activities, some of which require the performance of tedious tasks. Our goal is to develop a virtual proxy that would replace human participants in online virtual worlds. The proxy should be able to perform simple tasks on behalf of its owner, similar to the way the owner would have performed it. In this paper we focus on the challenge of social navigation. We use a data-driven approach based on recording human participants in the virtual environment; this training set, with a machine learning approach, is then used to control an agent in real time who is performing the same social task. We evaluate our method based on data collected from different participants. © 2011 Springer-Verlag.