History Encoding Representation Design for Human Intention Inference

TL;DR

This paper proposes a history encoding representation for human intention inference that is interpretable and effective, demonstrating its success through extensive experiments.

Contribution

It introduces a novel history encoding representation specifically designed for human intention prediction tasks, enhancing interpretability and prediction accuracy.

Findings

The history encoding approach improves prediction performance.

The representation is interpretable, aiding understanding of human intentions.

Experimental results confirm the effectiveness of the proposed method.

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.