Generative policies have recently been researched to provide a method for next generation security policies. They are created using either traditional machine learning techniques or, more recently, inductive learning of answer set programs. The latter method is targeted to the learning of Answer Set Grammars (ASG), a new notion of generative policy model for security policies that has the benefit of transparent explainability of the learned outcomes. This paper proposes a military scenario based on logistical resupply from a military base to coalition forces located in a nearby urban area or city. We describe the scenario and accompanying policy such that the context of the resupply missions (and therefore the policy) changes over time. The set of policies and related changes over time have been manually defined using a set of human created rules to replicate how security policies would currently be created by humans in such scenarios. We show how inductive learning of answer set programs can successfully learn ASG generative policy models that capture the human-driven rules from just example traces and decisions made at different time points and with respect to different contextual situations that can arise during the resupply mission. These results demonstrate the utility of ASG generative policy as a method for modelling human-driven policy rules.