Computing Plans that Signal Normative Compliance

There has been increasing acceptance that agents must act in a way that is sensitive to ethical considerations. These considerations have been cashed out as constraints, such that some actions are permissible, while others are impermissible. In this paper, we claim that, in addition to only performing those actions that are permissible, agents should only perform those courses of action that are _unambiguously_ permissible. By doing so they signal normative compliance: they communicate their understanding of, and commitment to abiding by, the normative constraints in play. Those courses of action (or plans) that succeed in signalling compliance in this sense, we term 'acceptable'. The problem this paper addresses is how to compute plans that signal compliance, that is, how to find plans that are acceptable as well as permissible. We do this by identifying those plans such that, were an observer to see only part of its execution, that observer would infer the plan enacted was permissible. This paper provides a formal definition of compliance signalling within the domain of AI planning, describes an algorithm for computing compliance signalling plans, provides preliminary experimental results and discusses possible improvements. The signalling of compliance is vital for communication, coordination and cooperation in situations where the agent is partially observed. It is equally vital, therefore, to solve the computational problem of finding those plans that signal compliance. This is what this paper does.