Cross-Population Semiosis in Multi-agent Systems

Semiosis mechanism in an artificial system should guarantee the development of a consistent and common substances of language symbols. Thus allowing a population of interacting agents to autonomously learn, adapt and optimise their semantics. In this research we define a settings for the cross-population semiosis model, which involves two or more mature populations set together in a common environment with a goal to align their predefined (or differently developed) lexicons. In particular, using the language game model we introduce a new type of language game scenario and define a set of specific measures that capture the dynamics of lexicon evolution in cross-population semiosis model. Finally we provide an experimental verification of the behaviour of the alignment process of cross-population semiosis, as such test the applicability of the classical language game model approach.

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