Norm Conflict Resolution in Stochastic Domains


  • Daniel Kasenberg Tufts University
  • Matthias Scheutz Tufts University



normative systems, Markov Decision Processes, Linear Temporal Logic, norm conflict


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.




How to Cite

Kasenberg, D., & Scheutz, M. (2018). Norm Conflict Resolution in Stochastic Domains. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).