  We consider communication when there is no agreement about symbols and
meanings. We treat it within the framework of reinforcement learning. We apply
different reinforcement learning models in our studies and simplify the problem
as much as possible. We show that the modelling of the other agent is
insufficient in the simplest possible case, unless the intentions can also be
modelled. The model of the agent and its intentions enable quick agreements
about symbol-meaning association. We show that when both agents assume an
`intention model' about the other agent then the symbol-meaning association
process can be spoiled and symbol meaning association may become hard.
