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A Latex template for the preparation of IAU Symposia Proceedings downloaded from http://www.iau.org/static/scientific_meetings/authors/. The package contains: Class File (iau.cls), Instructions, a Sample PDF and a Sample TeX file

Этот шаблон документа разработан в 2014 году Данилом Фёдоровых (danil@fedorovykh.ru) для использования в курсе «Документы и презентации в \LaTeX», записанном НИУ ВШЭ для Coursera.org: http://coursera.org/course/latex. Исходная версия шаблона — https://www.writelatex.com/coursera/latex/5.3

Этот шаблон документа разработан в 2014 году Данилом Фёдоровых (danil@fedorovykh.ru) для использования в курсе «Документы и презентации в \LaTeX», записанном НИУ ВШЭ для Coursera.org: http://coursera.org/course/latex. Исходная версия шаблона — https://www.writelatex.com/coursera/latex/5.2.1

This is an IEEE based template that can be used for presenting your work on the Open Science Data Cloud. Use it for the PIRE Workshop challenge and other submissions such as the Supercomputing 2014 conference.

This is the Persian IMC45 paper's template. این فایل قالب مقالات چهل و پنجمین کنفرانس ریاضی ایران است.

A basic template for seminar papers based on the IEEE transactions style. Instructions are in German, but the template supports German and English papers. Language can be changed using the babel package options.

A minimal example showing how to choose different options for section and subsection labelling (including upper and lower case Roman numerals) by redefining the appropriate commands. Source: http://www.latex-community.org/forum/viewtopic.php?f=19&t=17690

This guide explains how a user can interact with the system,right from the starting of login page upto the end of the process.

In nearly all videogames, creating smart and complex artificial agents helps ensure an enjoyable and challenging player experience. Using a dodgeball-inspired simulation, we attempt to train a population of robots to develop effective individual strategies against hard-coded opponents. Every evolving robot is controlled by a feedforward artificial neural network, and has a fitness function based on its hits and deaths. We evolved the robots using both standard and real-time NEAT against several teams. We hypothesized that interesting strategies would develop using both evolutionary algorithms, and fitness would increase in each trial. Initial experiments using rtNEAT did not increase fitness substantially, and after several thousand time steps the robots still exhibited mostly random movement. One exception was a defensive strategy against randomly moving enemies where individuals would specifically avoid the area near the center line. Subsequent experiments using the NEAT algorithm were more successful both visually and quantitatively: average fitness improved, and complex tactics appeared to develop in some trials, such as hiding behind the obstacle. Further research could improve our rtNEAT algorithm to match the relative effectiveness of NEAT, or use competitive coevolution to remove the need for hard-coded opponents.