Alexander Pudmenzky

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We introduce a novel way of measuring the entropy of a set of values undergoing changes. Such a measure becomes useful when analyzing the temporal development of an algorithm designed to numerically update a collection of values such as artificial neural network weights undergoing adjustments during learning. We measure the entropy as a function of the(More)
We investigate the amount of cooperation between agents in a population during reward collection that is required to minimize the overall collection time. In our computer simulation agents have the option to broadcast the position of a reward to neighboring agents with a normally distributed certainty. We modify the standard deviation of this certainty to(More)
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