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Learning and Obeying Conflicting Norms in Stochastic Domains

Artificial agents will need to be able to reason about and obey human moral and social norms. Additionally, agents must be able to learn norms, both from instruction (e.g., by natural language interaction with humans) and by observing the behavior of humans or other agents. This is necessary because (1) humans have many moral and social norms, perhaps too many to be able to pre-program; and (2) moral and social norms may vary between cultures, and across time. Both of these challenges – obeying moral and social norms, and learning them – are complicated by the fact that such norms may conflict. When an agent’s norms cannot all be satisfied, that agent should be able to “minimize the badness” of their behaviors in some principled way. Norm conflicts also make learning norms (e.g., from behavior) more difficult, since (1) the agent will need to learn preferences between norms as well as the norms themselves (in case of future conflict), and (2) the observed behaviors may be a compromise between inconsistent normative principles. Our work addresses the problem of learning (from behavior) and obeying potentially-conflicting norms in stochastic domains. In particular, we represent moral and social norms as statements in Linear Temporal Logic (LTL), and we employ the framework of Markov Decision Processes (MDPs).