Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering

• Main Authors: Lore, Kin Gwn, Sweet, Nicholas, Kumar, Kundan, Ahmed, Nisar, Sarkar, Soumik
• Format: Journal Article
• Language: English
• Published: 23-12-2015
• Subjects: Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.1512.07592

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.