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Semantic Dialogue Modeling
(2012)
Machine Semiotics
(2023)
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).
We present a model, inspired by quantum field theory, of the so-called inner stage of technical cognitive agents. The inner stage represents all knowledge of the agent. It allows for planning of actions and for higher cognitive functions like coping and fantasy. By the example of a cognitive mouse agent living in a maze wold, we discuss learning, action planning, and attention in a fully deterministic setting and assuming a totally observable world. We explain the relevance of our approach to cognitive infocommunications.
We present a Matlab toolbox, called “FockBox”, handling
Fock spaces and objects associated with Fock spaces: scalars, ket and
bra vectors, and linear operators. We give brief application examples
from computational linguistics, semantics processing, and quantum logic,demonstrating the use of the toolbox.
Zunächst möchten wir in das Thema einführen, indem wir kurz auf
gegenwärtige Probleme und zukünftige Möglichkeiten von Sprachassistenten eingehen. Es folgen grundlegende Überlegungen zur Semantik und zwei fundamentale Erkenntnisse aus der Biosemiotik und dem Konstruktivismus. Danach fokussieren wir uns auf den Spracherwerbsprozess. Hierbei kommt ein Petrinetz zum Einsatz, das einem elementaren, bereits in früheren Arbeiten vorgestellten, Verstärkungsalgorithmus folgt. Dem schließt sich eine Demonstration des Spracherwerbs anhand eines Beispiels aus dem Smart Car-Bereich an.
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