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Networks Do Matter: The Socially Motivated Design of a 3D Race Controller Using Cultural Algorithms

Networks Do Matter: The Socially Motivated Design of a 3D Race Controller Using Cultural Algorithms

Robert G. Reynolds
Copyright: © 2010 |Volume: 1 |Issue: 1 |Pages: 25
ISSN: 1947-9263|EISSN: 1947-9271|ISSN: 1947-9263|EISBN13: 9781609600020|EISSN: 1947-9271|DOI: 10.4018/jsir.2010010102
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MLA

Reynolds, Robert G. "Networks Do Matter: The Socially Motivated Design of a 3D Race Controller Using Cultural Algorithms." IJSIR vol.1, no.1 2010: pp.17-41. http://doi.org/10.4018/jsir.2010010102

APA

Reynolds, R. G. (2010). Networks Do Matter: The Socially Motivated Design of a 3D Race Controller Using Cultural Algorithms. International Journal of Swarm Intelligence Research (IJSIR), 1(1), 17-41. http://doi.org/10.4018/jsir.2010010102

Chicago

Reynolds, Robert G. "Networks Do Matter: The Socially Motivated Design of a 3D Race Controller Using Cultural Algorithms," International Journal of Swarm Intelligence Research (IJSIR) 1, no.1: 17-41. http://doi.org/10.4018/jsir.2010010102

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Abstract

This article describes a socially motivated evolutionary algorithm, Cultural Algorithms, to design a controller for a 3D racing game for use in a competitive event held at the 2008 IEEE World Congress. The controller was modeled as a state machine and a set of utility functions were associated with actions performed in each state. Cultural Algorithms are used to optimize these functions. Cultural Algorithms consist of a Population Space, a collection of knowledge sources in the Belief Space, and a communication protocol connecting the components together. The knowledge sources in the belief space vie to control individuals in the population through the social fabric influence function. Here the population is a network of chromosomes connected by the LBest topology. This LBest configuration was employed to train the system on an example oval track prior to the contest, but it did not generalize to other tracks. The authors investigated how other topologies performed when learning on each of the contest tracks. The square network (a type of small world network) worked best at distributing the influence of the knowledge sources, and reduced the likelihood of premature convergence for complex tracks.

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