Computational framework for modeling the dynamic evolution of large-scale multi-agent organizations
Alina Lazar, Robert G. Reynolds · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
A multi-agent system model of the origins of an archaic state is developed. Agent interaction is mediated by a collection of rules. The rules are mined from a related large-scale data base using two different techniques. One technique uses decision trees while the other uses rough sets. The latter was used since the data collection techniques were associated with a certain degree of uncertainty. The generation of the rough set rules was guided by Genetic Algorithms. Since the rules mediate agent interaction, the rule set with fewer rules and conditionals to check will make scaling up the simulation easier to do. The results suggest that explicitly dealing with uncertainty in rule formation can produce simpler rules than ignoring that uncertainty in situations where uncertainty is a factor in the measurement process.