TY - GEN
T1 - Finding forms of flocking
T2 - 11th International Workshop on Multi-Agent-Based Simulation, MABS 2010, Co-located with the 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010
AU - Stonedahl, Forrest
AU - Wilensky, Uri
N1 - Funding Information:
Acknowledgments. We especially wish to thank William Rand for constructive feedback on this research, Luis Amaral for generously providing computational resources to carry out our experiments, and the National Science Foundation for supporting this work (grant IIS-0713619).
PY - 2011
Y1 - 2011
N2 - While agent-based models (ABMs) are becoming increasingly popular for simulating complex and emergent phenomena in many fields, understanding and analyzing ABMs poses considerable challenges. ABM behavior often depends on many model parameters, and the task of exploring a model's parameter space and discovering the impact of different parameter settings can be difficult and time-consuming. Exhaustively running the model with all combinations of parameter settings is generally infeasible, but judging behavior by varying one parameter at a time risks overlooking complex nonlinear interactions between parameters. Alternatively, we present a case study in computer-aided model exploration, demonstrating how evolutionary search algorithms can be used to probe for several qualitative behaviors (convergence, non-convergence, volatility, and the formation of vee shapes) in two different flocking models. We also introduce a new software tool (BehaviorSearch) for performing parameter search on ABMs created in the NetLogo modeling environment.
AB - While agent-based models (ABMs) are becoming increasingly popular for simulating complex and emergent phenomena in many fields, understanding and analyzing ABMs poses considerable challenges. ABM behavior often depends on many model parameters, and the task of exploring a model's parameter space and discovering the impact of different parameter settings can be difficult and time-consuming. Exhaustively running the model with all combinations of parameter settings is generally infeasible, but judging behavior by varying one parameter at a time risks overlooking complex nonlinear interactions between parameters. Alternatively, we present a case study in computer-aided model exploration, demonstrating how evolutionary search algorithms can be used to probe for several qualitative behaviors (convergence, non-convergence, volatility, and the formation of vee shapes) in two different flocking models. We also introduce a new software tool (BehaviorSearch) for performing parameter search on ABMs created in the NetLogo modeling environment.
KW - ABM
KW - agent-based modeling
KW - flocking
KW - genetic algorithms
KW - model exploration
KW - multi-agent simulation
KW - parameter search
UR - http://www.scopus.com/inward/record.url?scp=79952019491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952019491&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-18345-4_5
DO - 10.1007/978-3-642-18345-4_5
M3 - Conference contribution
AN - SCOPUS:79952019491
SN - 9783642183447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 75
BT - Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers
Y2 - 11 May 2010 through 11 May 2010
ER -