Augmented reasoning in the mirror world

In order to enable a social agent to behave in a believable and realistic way, it needs a wide range of information in the form of both low-level value-based data as well as high-level semantic knowledge. In this work we propose a system that puts a virtual reality layer between the real world and an agent's knowledge representation. This mirror world allows the agent to use its abstract representation of the environment and inferred events as an additional source of knowledge when reasoning about the real world. Additionally, users and developers can use the mirror world, with its visualized data and highlighting of the agent's reasoning, for further understanding of the agent's behavior, debugging and testing, or the simulation of additional sensor input.