General Game Playing (GGP) is the playing of a wide variety games you may have never seen before, by being told nothing but the rules of the game at run time. This sets it apart from traditional specific game players, like the famous chess player Deep Blue. Deep Blue can beat the world chess champion at chess however, it has absolutely no idea how to play checkers. It is designed for one particular game and cannot adapt to rule changes, and certainly cannot play entirely different games. The goal of this project is to create a program that will play a wide variety of 2d games given descriptions of their rules without the creator of the program having ever known of the games. This report will cover the design and implementation of this project, as well as the background research performed and reflections on the outcome of the project.
The knowing of politics is mostly an unknown problem on a large scale in Colombia. Here is proposed a web visualization project, in which the historical information of the votes and the elected representatives are presented in an entertaining and inclusive way, in order to generate a feeling of empathy or politician relevance in the spectator creating the assumption that there's a familiar relationship
Understanding dating behavior is intrinsically interesting and practically important for every individual in the society. People want to find someone who they want to share stories and emotions, understand and sympathize, commit the rest of life together. Accordingly, people spend a huge part of life finding "the one" or "soul-mate" who they believe potentially maximize their happiness and satisfaction in life. Ironically, they often end up breaking up and saying "He/She was not the right person". Researchers in many areas have studied dating behavior in varying ways to understand why people repeat the vicious circle and still get in there to find the right person. Here, we investigated dating behavior by analyzing relations between multi-aspect variables that include physical and psychological features of individuals and the probability of match in a speed-dating situation. We used theoretical approach and machine learning approach to investigate the pattern of dating behavior and to find the best predictor of match in dataing. For theory driven approach, we used multilevel linear model and multilevel logistic regression. For machine-learning approach, we used learning vector quantization and extreme gradient boosting.