This extended abstract discusses various approaches to the constraining of

Partially Observable Markov Decision Processes (POMDPs) using social norms and

logical assertions in a dynamic logic framework. Whereas the exploitation of

synergies among formal logic on the one hand and stochastic approaches and machine learning on the other is gaining significantly increasing interest since several years, most of the respective approaches fall into the category

of relational learning in the widest sense, including inductive

(stochastic) logic programming. In contrast, the use of formal knowledge (including knowledge about social norms) for the provision of hard constraints

and prior knowledge for some stochastic learning or modeling task is

much less frequently approached. Although we do not propose directly implementable technical solutions, it is hoped that this work is

a useful contribution to a discussion about the usefulness

and feasibility of approaches from norm research and formal

logic in the context of stochastic behavioral models, and vice versa.