In the context of human-supervised autonomy, we study the problem of optimal fidelity
selection for a human operator performing an underwater visual search task. Human …

What makes humans so good at solving seemingly complex video games? Unlike
computers, humans bring in a great deal of prior knowledge about the world, enabling …

Exploration and reward specification are fundamental and intertwined challenges for
reinforcement learning. Solving sequential decision making tasks with a non-trivial element …

With the tremendous increase in video data size, search performance could be impacted
significantly. Specifically, in an interactive system, a real-time system allows a user to …

Real-time collaboration with humans poses challenges due to the different behavior patterns
of humans resulting from diverse physical constraints. Existing works typically focus on …

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent
altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the …

Learning requires both study and curiosity. A good learner is not only good at extracting
information from the data given to it, but also skilled at finding the right new information to …

In a search and rescue scenario, rescuers may have different knowledge of the environment
and strategies for exploration. Understanding what is inside a rescuer's mind will enable an …

The diversity of intrinsic qualities of multimedia entities tends to impede their effective
retrieval. In a Self-Learning Search Engine architecture, the subtle nuances of human …

Deep reinforcement learning methods traditionally struggle with tasks where environment
rewards are particularly sparse. One successful method of guiding exploration in these …