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Feature: 'Using Particle Swarm Optimization for Offline Training in a Racing Game'

In another of today's main Gamasutra features, I-Imagine developer Etienne de Villiers discusses how he used the Particle Swarm Optimization algorithm to arrive at good v...
In another of today's main Gamasutra features, I-Imagine developer Etienne de Villiers discusses how he used the Particle Swarm Optimization algorithm to arrive at good values for the physics and AI parameters of his racing game. De Villiers explains of the concept: "Many contemporary games use a data-driven approach to control various aspects of a game. In a racing game, for example, an external file may be used to specify the values for various physics parameters of a vehicle, or the behavioral parameters of an AI opponent. Deciding on values for these parameters can be a difficult task, since a change made to one parameter value might affect several aspects of behavior. Although it is sometimes possible to calculate the ideal values mathematically, in most cases a trial and error approach needs to be used. In this article, I will discuss how we have used the Particle Swarm Optimization (PSO) algorithm to arrive at good values for [AI and physics]. The first part of this article provides a quick overview of function optimization. This is followed by a discussion of the PSO algorithm, after which I will show how we have used PSO within our racing game." You can now read the full Gamasutra feature on the subject, including more on suggested technical solutions for this potentially tricky problem (no registration required, please feel free to link to the article from external websites).

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