Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering
This work addresses the problem of optimizing human-machine collaboration in sensing tasks, which is incremental as it builds on existing VOI methods with a novel deep learning approach.
The paper tackles the challenge of efficiently scheduling interactions with human sensors in collaborative human-machine information gathering without causing task overload, by introducing a deep learning framework for Value of Information estimation that achieves computationally efficient online inference and minimal policy tuning, demonstrated on a mobile robotic search problem with semantic inputs.
Effective human-machine collaboration can significantly improve many learning and planning strategies for information gathering via fusion of 'hard' and 'soft' data originating from machine and human sensors, respectively. However, gathering the most informative data from human sensors without task overloading remains a critical technical challenge. In this context, Value of Information (VOI) is a crucial decision-theoretic metric for scheduling interaction with human sensors. We present a new Deep Learning based VOI estimation framework that can be used to schedule collaborative human-machine sensing with computationally efficient online inference and minimal policy hand-tuning. Supervised learning is used to train deep convolutional neural networks (CNNs) to extract hierarchical features from 'images' of belief spaces obtained via data fusion. These features can be associated with soft data query choices to reliably compute VOI for human interaction. The CNN framework is described in detail, and a performance comparison to a feature-based POMDP scheduling policy is provided. The practical feasibility of our method is also demonstrated on a mobile robotic search problem with language-based semantic human sensor inputs.