  Artificial agents will need to be aware of human moral and social norms, and
able to use them in decision-making. In particular, artificial agents will need
a principled approach to managing conflicting norms, which are common in human
social interactions. Existing logic-based approaches suffer from normative
explosion and are typically designed for deterministic environments;
reward-based approaches lack principled ways of determining which normative
alternatives exist in a given environment. We propose a hybrid approach, using
Linear Temporal Logic (LTL) representations in Markov Decision Processes
(MDPs), that manages norm conflicts in a systematic manner while accommodating
domain stochasticity. We provide a proof-of-concept implementation in a
simulated vacuum cleaning domain.
