  Recognizing a basic difference between the semiotics of humans and machines
presents a possibility to overcome the shortcomings of current speech assistive
devices. For the machine, the meaning of a (human) utterance is defined by its
own scope of actions. Machines, thus, do not need to understand the
conventional meaning of an utterance. Rather, they draw conversational
implicatures in the sense of (neo-)Gricean pragmatics. For speech assistive
devices, the learning of machine-specific meanings of human utterances, i.e.
the fossilization of conversational implicatures into conventionalized ones by
trial and error through lexicalization appears to be sufficient. Using the
quite trivial example of a cognitive heating device, we show that - based on
dynamic semantics - this process can be formalized as the reinforcement
learning of utterance-meaning pairs (UMP).
