bourahla2023a (2024-02-10)

Artificial agents, as humans, use their knowledge to behave in an environment and within a society. Humans evolve their knowledge by adapting it in response to interactions with their environment and society. The question that is raised in this thesis is:``can knowledge evolve in a society of artificial agents, as it does in a human society?'' In particular, if agents adapt to improve their social interactions, how can this affect the quality of the population's knowledge about the environment? And how does it affect knowledge diversity? To address the questions, ontology evolution is simulated based on principles from experimental cultural evolution through an experimental framework in which: agents initially learn ontologies, from object samples, which they later adapt by interacting with each other about objects in the environment. Using this experimental framework, we show that: (1) agents reach a state of agreement in their interactions, (2) they improve the quality of their knowledge about the environment, and (3) they preserve the diversity of their knowledge. In order to characterise knowledge evolution through multiple generations, experiments are conducted with agents endowed with reproduction capabilities. Results show that (1) the variation provided by inter-generation transmission allows agents to further improve the quality of their ontologies; (2) agents select the knowledge to be preserved through intra-generation transmission which compensates for the lack of teacher selection in inter-generation transmission; and finally, (3) diversity remains stable from one generation to another. This work not only provides a basis for implementing agents capable of culturally evolving their knowledge, but also suggests that simulating such behavior can serve as a valuable tool for testing hypotheses about human cultural knowledge evolution.