History Encoding Representation Design for Human Intention Inference

Abstract

In this extended abstract, we investigate the design of learning representation for human intention inference. In our designed human intention prediction task, we propose a history encoding representation that is both interpretable and effective for prediction. Through extensive experiments, we show our prediction framework with a history encoding representation design is successful on the human intention prediction problem.