Identifying Norms from Observation Using MCMC Sampling

To promote efficient interactions in dynamic and multi-agent systems, there is much interest in techniques that allow agents to represent and reason about social norms that govern agent interactions. Much of this work assumes that norms are provided to agents, but some work has investigated how agents can identify the norms present in a society through observation and experience. However, the norm-identification techniques proposed in the literature often depend on a very specific and domain-specific representation of norms, or require that the possible norms can be enumerated in advance. This paper investigates the problem of identifying norm candidates from a normative language expressed as a probabilistic context-free grammar, using Markov Chain Monte Carlo (MCMC) search. We apply our technique to a simulated robot manipulator task and show that it allows effective identification of norms from observation.