Before revision:  We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals---things that did not happen but could have under different conditions---they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models.
After revision:  We ground the notion of opportunity chains in human-agent experiment, we define a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models.