Reinforcement learning of utterance semantics for speech-assisted cognitive user interfaces

Talk at session "Language Meaning: between Symbolic and Statistical Modelling" of the Leibniz MMS Days 2025. Recognizing a basic difference between the semiotics of humans and machines presents a possibility to overcome the shortcomings of current artificial intelligence (AI) 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 AI 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).