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Autonomous learning in an information stream through autopoiesis

Nootropia is a complex, self-organizing system, inspired by the Theory of Autopoiesis and successfully applied so far to the challenging problem of profiling a user’s information interests. In this paper for the first time, Nootropia is studied in the context of Artificial Life, as an autonomous system that can learn without human intervention. A series of experiments demonstrate that Nootropia can autonomously learn to identify documents belonging to a specific topic with minimal training. This is achieved through a deterministic process of self-organization, which, when coupled with a complex and dynamic information environment, gives rise to rich and unpredictable behavior. Nootropia is open to its environment and operates far from equilibrium, while it tries to maintain its identity within an information stream. Our exploration of the dynamics behind Nootropia’s autonomous learning capabilities lead to interesting insights, which may extend beyond its successful application to the problem of profiling and towards a new research stream that uses Nootropia as a means for studying computational autopoiesis.