DNNs get more accurate as the size and coverage of training data increase [17]. While investing in high-quality and large-scale labeled data is one path, another is utilizing prior knowledge - concisely referred to as'rules': reasoning heuristics, equations, associative logic, constraints or blacklists.

Of the useful features, which ones does the model use? Of the task-irrelevant features, which ones does the model represent?

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management.

Network outputs can change indirectly to unexpected values after any random batch update for input data not included in the batch, called churn in this paper.