A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
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 widestdoi:10.5445/ir/1000092188 fatcat:mxix3zlmrvdmdl7uhppxy2xrx4