Paolo di Prodi

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We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop simulation of the system and compare this(More)
This thesis develops a theoretical framework for the generation of artificial societies. In particular it shows how sub-systems emerge when the agents are able to learn and have the ability to communicate. This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated originally by the German sociologist Luhmann, with(More)
It has been shown that plasticity is not a fixed property but, in fact, changes depending on the location of the synapse on the neuron and/or changes of biophysical parameters. Here, we investigate how plasticity is shaped by feedback inhibition in a cortical microcircuit. We use a differential Hebbian learning rule to model spike-timing-dependent(More)
We introduce a new theoretical framework, based on Shannon’s communication theory and on Ashby’s law of requisite variety, suitable for artificial agents using predictive learning. The framework quantifies the performance constraints of a predictive adaptive controller as a function of its learning stage. In addition, we formulate a practical measure, based(More)
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