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).

翻译：识别人类与机器符号学之间的根本差异为克服当前语音辅助设备的局限性提供了可能。对机器而言，（人类）话语的意义由其自身的行动范围所界定。因此，机器无需理解话语的传统意义，而是依据（新）格莱斯语用学推导出会话含义。对于语音辅助设备而言，学习人类话语的机器特定意义——即通过试错法将会话含义固化为规约含义并借助词汇化过程实现——似乎已足够。我们以认知供暖设备这一简明示例表明，基于动态语义学，该过程可被形式化为话语-意义对（UMP）的强化学习。