  Within Reinforcement Learning, there is a fledgling approach to
conceptualizing the environment in terms of predictions. Central to this
predictive approach is the assertion that it is possible to construct
ontologies in terms of predictions about sensation, behaviour, and time---to
categorize the world into entities which express all aspects of the world using
only predictions. This construction of ontologies is integral to predictive
approaches to machine knowledge where objects are described exclusively in
terms of how they are perceived. In this paper, we ground the Pericean model of
semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three
Categories in the notation of General Value Functions. Using the Peircean model
of semiotics, we demonstrate that predictions alone are insufficient to
construct an ontology; however, we identify predictions as being integral to
the meaning-making process. Moreover, we discuss how predictive knowledge
provides a particularly stable foundation for semiosis\textemdash the process
of making meaning\textemdash and suggest a possible avenue of research to
design algorithmic methods which construct semantics and meaning using
predictions.
