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LaTeX template, based on ntnuthesis class, for PhD theses at Norwegian University of Science and Technology (NTNU) Trondheim.
This template was originally published on ShareLaTeX and subsequently moved to Overleaf in November 2019.
This document was modified from the latex template "NTU CNYSP Research Report Template (CY1400-CY2001-CY2002)" authored by Karn Watcharasupat. This is a simplified version to be used by ASE students during their FYP.
This is not a full thesis template! It only demonstrates how to create per-chapter references using the chapterbib package with BibTeX. (Do not use with BibLaTeX!)
This guide covers preparing for installation, running the installation script, and the steps that should be done after the installation script has completed.
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.
Lesvoorbereidingsformulier voor vakdidactiek informatica binnen de Educatieve master Wetenschappen & Technologie aan de KU Leuven.
Dit is de versie voor 2021 - 2022
Jesse Hoobergs
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