Machine Semiotics

TL;DR

This paper proposes a new approach to speech assistive devices by modeling machine understanding through pragmatic implicatures and reinforcement learning, bypassing traditional semantic comprehension.

Contribution

It introduces a machine semiotics framework that formalizes learning of utterance meanings via reinforcement of implicatures, contrasting with conventional semantic models.

Findings

Machine meanings are defined by actions, not conventional semantics.

Reinforcement learning can encode implicatures into conventionalized meanings.

The approach is demonstrated with a cognitive heating device example.

Abstract

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).