TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Abstract: We often encounter situations in which an experimenter wants to find, by sequential experimentation, xmax = arg maxx f(x), where f(x) is a (possibly unknown) function of a well controllable variable x. Taking inspiration from physics and engineering, we have designed a new method to address this problem. In this paper, we first introduce the method in continuous time, and then present two algorithms for use in sequential experiments. Through a series of simulation studies, we show that the method is effective for finding maxima of unknown functions by experimentation, even when the maximum of the functions drifts or when the signal to noise ratio is low.

SOLUTION: Thompson sampling with the online bootstrap

PROBLEM: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­ other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do­ ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de­ pendencies as those used by certain algorithms for learn­ ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for­ warded. Finally, methods for further testing the accu­ racy of these claims are discussed.

SOLUTION: THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE

PROBLEM: In Chile, does not exist an independent entity that publishes quantitative or qualitative surveys to   understand   the   traditional   media   environment   and   its   adaptation   on   the   Social  Web. Nowadays, Chilean newsreaders are increasingly using social web platforms as their primary source of   information,  among which Twitter  plays  a central   role.  Historical  media and pure players  are developing  different  strategies   to   increase  their  audience  and  influence on  this platform. In this article, we propose a methodology based on data mining techniques to provide a first level of analysis of the new Chilean media environment. We use a crawling technique to mine news streams of  37 different  Chilean media actively  presents on Twitter  and propose several  indicators  to compare  them. We analyze their  volumes of production,   their  potential audience, and using NLP techniques, we explore the content of their production: their editorial line and their geographic coverage.

SOLUTION:
Diagnosing editorial strategies of Chilean media on Twitter using an automatic news classifier