Modelling the Socialization of Creative Agents in a Master-Apprentice Setting: The Case of Movie Title Puns
This addresses the problem of social hierarchy in creative AI systems for researchers in computational social creativity, but it is incremental as it builds on existing agent-based models.
The paper tackles the problem of modeling socialization in computational creativity by studying master-apprentice pairs, where a genetic algorithm master influences an NMT-based apprentice, and finds that different parenting styles affect creative output, though no concrete numbers are provided.
This paper presents work on modelling the social psychological aspect of socialization in the case of a computationally creative master-apprentice system. In each master-apprentice pair, the master, a genetic algorithm, is seen as a parent for its apprentice, which is an NMT based sequence-to-sequence model. The effect of different parenting styles on the creative output of each pair is in the focus of this study. This approach brings a novel view point to computational social creativity, which has mainly focused in the past on computationally creative agents being on a socially equal level, whereas our approach studies the phenomenon in the context of a social hierarchy.