Partially Observable Markov Decision Processes with Behavioral Norms

Abstract

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.