  Law codes and regulations help organise societies for centuries, and as AI
systems gain more autonomy, we question how human-agent systems can operate as
peers under the same norms, especially when resources are contended. We posit
that agents must be accountable and explainable by referring to which rules
justify their decisions. The need for explanations is associated with user
acceptance and trust. This paper's contribution is twofold: i) we propose an
argumentation-based human-agent architecture to map human regulations into a
culture for artificial agents with explainable behaviour. Our architecture
leans on the notion of argumentative dialogues and generates explanations from
the history of such dialogues; and ii) we validate our architecture with a user
study in the context of human-agent path deconfliction. Our results show that
explanations provide a significantly higher improvement in human performance
when systems are more complex. Consequently, we argue that the criteria
defining the need of explanations should also consider the complexity of a
system. Qualitative findings show that when rules are more complex,
explanations significantly reduce the perception of challenge for humans.
