Nomenclatural reinforcements

Cumulative Learning Through Intrinsic Reinforcements

Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing reinforcement learning artificial agents with a learning signal that resembles the characteristic of the phasic activations of dopaminergic neurons would be an advancement in the development o...

The Structure – Permeability Relation of Textile Reinforcements

Effects of Intervals between Reinforcements

Multi-particle processes with reinforcements

The multi-particle generalization of the edge-reinforced random walk is stated. Some recurrence results are obtained.

A Logic Approach to Modeling Nomenclatural Change

— We utilize an Answer Set Programming (ASP) approach to show that the principles of nomenclature are tractable in computational logic. To this end we design a hypothetical, 20 nomenclatural taxon use case with starting conditions that embody several overarching principles of the International Code of Zoological Nomenclature; including Binomial Nomenclature, Priority, Coordination, Homonymy, Ty...