History Encoding Representation Design for Human Intention Inference
This work addresses human intention prediction, but appears incremental as it builds on existing frameworks with a new representation design.
The paper tackled the problem of human intention inference by proposing a history encoding representation that is interpretable and effective, showing success in prediction through extensive experiments.
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.