AbstractThe natural languages that underlie human communication are remarkably expressive, robust and well-adapted to the communicative needs of their users. However, the question of how these languages have emerged and through which mechanisms they continue to evolve remains heavily debated. A modern methodology for studying this question is to simulate the emergence and evolution of language using agent-based models. In these models, a population of autonomous agents, which are either physical robots or software entities, participates in a series of communicative interactions, during which they develop their own language. The goal of the models is to determine the exact mechanisms that need to be present in the individual agents, so that a communication system with human language-like properties can emerge and evolve. This dissertation presents three major contributions to the field of research that models the emergence and evolution of grammatical structures.
The first contribution consists in the implementation of a higher-level notation for Fluid Construction Grammar (FCG), a computational grammar formalism that is often used in evolutionary linguistics experiments. The second contribution presents powerful mechanisms for introducing, adopting and aligning grammatical structures through local interactions. The third contribution consists in a case study that demonstrates how the representations and mechanisms introduced by the first two contributions can be applied in a concrete agent-based experiment. The case study investigates how early syntactic structures can emerge and evolve in a population of agents. More specifically, the experiment models how shared word order patterns can emerge and evolve, and reduce the referential ambiguity of the language.
|Date of Award||4 Oct 2018|
|Supervisor||Luc Steels (Promotor) & Katrien Beuls (Promotor)|
- Fluid Construction Grammar
- Evolutionary Linguistics
- Language Evolution
- Emergence of Syntax
- Meta-level Learning