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Proyecto de grado
Author: Victor Brena
Description: Contains the thesis template using memoir class,
which is mainly based on book class but permits better control of
chapter styles for example. This template is an adaptation and
modification of Oscar's.
Memoir is a flexible class for typesetting poetry, fiction,
non-fiction and mathematical works as books, reports, articles or
manuscripts. CTAN repository is found at:
UoB guidelines for thesis presentation were found at:
The dissertation must be printed on A4 white paper. Paper up to A3 may be used
for maps, plans, diagrams and illustrative material. Pages (apart from the
preliminary pages) should normally be double-sided.
Memoir class loads useful packages by default (see manual).
Evolving robots to play dodgeball
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.
Daniel and Uriel