Share Email Print

Proceedings Paper

Computational framework for modeling the dynamic evolution of large-scale multi-agent organizations
Author(s): Alina Lazar; Robert G. Reynolds
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 15 July 2002
PDF: 13 pages
Proc. SPIE 4716, Enabling Technologies for Simulation Science VI, (15 July 2002); doi: 10.1117/12.474902
Show Author Affiliations
Alina Lazar, Wayne State Univ. (United States)
Robert G. Reynolds, Wayne State Univ. (United States)

Published in SPIE Proceedings Vol. 4716:
Enabling Technologies for Simulation Science VI
Alex F. Sisti; Dawn A. Trevisani, Editor(s)

© SPIE. Terms of Use
Back to Top