  Agents operating in unstructured environments often produce negative side
effects (NSE), which are difficult to identify at design time. While the agent
can learn to mitigate the side effects from human feedback, such feedback is
often expensive and the rate of learning is sensitive to the agent's state
representation. We examine how humans can assist an agent, beyond providing
feedback, and exploit their broader scope of knowledge to mitigate the impacts
of NSE. We formulate this problem as a human-agent team with decoupled
objectives. The agent optimizes its assigned task, during which its actions may
produce NSE. The human shapes the environment through minor reconfiguration
actions so as to mitigate the impacts of the agent's side effects, without
affecting the agent's ability to complete its assigned task. We present an
algorithm to solve this problem and analyze its theoretical properties. Through
experiments with human subjects, we assess the willingness of users to perform
minor environment modifications to mitigate the impacts of NSE. Empirical
evaluation of our approach shows that the proposed framework can successfully
mitigate NSE, without affecting the agent's ability to complete its assigned
task.
