Publication 6791

Ponticorvo M., Parisi D. & Miglino O. (2008) The autopoietic nature of the “inner world”: A study with evolved “blind” robots. In: Pezzulo G., Butz M. V., Sigaud O. & Baldassarre G. (eds.) Anticipatory behavior in adaptive learning systems: From psychological theories to artificial cognitive systems. Springer, Berlin: 115–131. Fulltext at
In this paper we propose a model of anticipatory behavior in robots which lack any sort of external stimulation. It would seem that in order to foresee an event and produce an anticipatory action an organism should receive some input from the external environment as a basis to predict what comes next. We ask if, even in absence of external stimulation, the organism can derive this knowledge from an “inner” world which “resonates” with the external world and is built up by an autopoietic process. We describe a number of computer simulations that show how the behavior of living organisms can reflect the particular characteristics of the environment in which they live and can be adaptive with respect to that environment even if the organism obtains extremely little information from the environment through its sensors, or no information at all. We use the Evorobot simulator to evolve a population of artificial organisms (software robots) with the ability to explore a square arena. Results indicate that sensor-less robots are able to accomplish this exploration task by exploiting three mechanisms: (1) they rely on the internal dynamics produced by recurrent connections; (2) they diversify their behavior by employing a larger number of micro-behaviors; (3) they self-generate an internal rhythm which is coupled to the external environment constraints. These mechanisms are all mediated by the robot’s actions.


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