A Framework to Even-Out Racetrack Bias
Article No.: 72, Pages 1 - 8
Abstract
Procedural Content Generation (PCG) has demonstrated its capability to create compelling game content across various domains, including racing games. In this paper, a novel approach utilizing PCG is presented that aims to generate racetracks with the primary objective of ensuring fairness and balanced gameplay regardless of the player’s vehicle choice1. The proposed framework comprises three distinct phases: During the first phase, modular racetrack segments are procedurally generated in order to enhance track variety, which plays a crucial role in providing an engaging gaming experience. In the second phase, AI car controllers are employed on different vehicles to simulate driving through all generated racetrack segments, gathering various statistics. This step allows for the collection of data that will inform decision-making during the final assembly of the complete racetrack. Finally, in the third phase, the collected data is utilized to select and assemble the most suitable racetrack segments, ensuring fairness for all simulated vehicle types by avoiding any potential unfair advantages or disadvantages. To further support this innovative framework, its theoretical foundation is explored, and detailed explanations of each step involved in the process are provided.
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May 2024
644 pages
ISBN:9798400709555
DOI:10.1145/3649921
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Published: 05 July 2024
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