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ABSTRACT This paper discusses the use of a Genetic Algorithm (GA) to evolve waypoint-based racing lines for computer-controlled cars in an online game. One goal is the reduction of developer effort, in order to automatically produce varied and effective racing lines for numerous different vehicle configurations on various racetracks. Another goal is to assess the design of fitness functions in order to evolve varying strategies and driving styles for the computer-controlled drivers. The effectiveness of this approach is assessed, and the evolved driving strategies on a number of specific racetracks are analysed.
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im sure science will keep working on methods to make it though. aside from destruction can you imagine a better source of pure, powerful energy? even a small amount could keep a space vessel going for a long time.
Recent years have shown a rising popularity of procedurally generated content, such as automated level design. To ensure the player enjoys the content, game developers need to make sure it is suitably difficult. This is challenging if at all possible when the content is generated after the product has shipped. The designers need to make sure the game can automatically alter the variables that control the difficulty, depending on the performance of the player. To determine which variables used in level generation control difficulty, a difficulty model is required. We attempt to find such a model for the racing game genre.
To identify what parts of the track most define the difficulty, we use two approaches. First, a data driven model, which uses machine learning to recognize difficult sections on the track. Second, an analytical model that attempts to predict where cars are most likely to lose traction, following the rules of physics. Using a custom-made racing game, our methods are tested empirically through player testing on various procedurally generated racetracks.
Results show that while we can not perfectly predict all difficult sections of a racetrack, crashes can indeed be predicted with above-average accuracy (over 60%) using simple algorithms, with relatively sparse data. The varying level of player performance is identified as one of the most influential reasons why accurate predictions are very hard to achieve. Further analysis of the data suggests some increased accuracy may potentially be achieved with slightly altered approaches.
Our exploratory work helps game developers identify at least the most problematic sections of tracks. We also believe it can be used as a foundation upon which further work can be based.
I’m not arguing that there is no place for any kind of randomness in game design. In fact, I argue strongly in favor of well-balanced, low-variance input randomness in multiplayer games. And single player games require input randomness."
100% chance (all randomness; e.g., Chutes & Ladders) can make it hard for people above the age of 6 to enjoy.
“'I think I’d rather tell someone I robbed a bank than tell them I procrastinate,' [Mr. Lockwood] says."
"...but I just haven't gotten around to studying those blueprints showing the vault's weak spots." :)
- Medical College of WisconsinBiostatistician, 1999 - present
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