Machine Semiotics
This addresses the shortcomings of current speech assistive devices for users by offering a novel approach to machine interpretation, though it appears incremental in applying existing theories to a specific domain.
The paper tackles the problem of improving speech assistive devices by proposing that machines interpret human utterances based on their own action scope, not conventional meaning, and demonstrates this through formalizing it as reinforcement learning of utterance-meaning pairs using a cognitive heating device example.
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).