FlatLand Benchmark Research
is a prototype two-dimensional (2-D) multi-agent world inspired primarily by the
virtual worlds met in computer games and by biological aspects like animal
foraging and mate-seeking. The main purpose of this simulated world is to be
used as a test bed environment for investigating evolutionary and gradient-based
(to a lesser degree) learning techniques and furthermore, their ability to
generate cooperative obstacle-avoidance and target-achievement behaviors.
Flatland is a complex environment in
that its properties depend on the interactions between agents—moreover, the
dynamics of agent behavior depend on discontinuously changing relationships
between the agents as they prove their double task. In that sense, complexity in
Flatland is a feature determined by the number of agents present.
Further interested in FlatLand
Papers about FlatLand
- G. N. Yannakakis, J. Levine, and J. Hallam, "Emerging
Cooperation with Minimal Effort. Rewarding over Mimicking,'' IEEE
Transactions on Evolutionary Computation, vol. 11, issue 3, pp. 382-396,
June 2007. [pdf]
- G. N. Yannakakis, J. Hallam and J. Levine, ''Evolutionary
Computation Variants for Cooperative Spatial Coordination,'' in
Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pp.
2715-2722, Edinburgh, UK, 2005. [pdf]
- G. N. Yannakakis, J. Levine, J. Hallam, and M.
robustness and effort cost comparison of machine learning mechanisms in FlatLand,'' in
Proceedings of the 11th Mediterranean Conference
on Control and Automation MED'03. IEEE, June 2003. [pdf]
FlatLand Simulations available for download
Instructions: Download and unzip the
files. Run MAGIA_GR.exe
N.B. Windows Vista do not support
fullscreen mode for DOS-applications. Go through
these guidelines if you want to view FlatLand simulations in Windows Vista.