Artificial neural networks as meta-models of combat processes: Applications to line-of-sight computations

摘要

Modeling high resolution combat is a computationally intensive activity that often requires compromise in the completeness or fidelity of the model to accommodate existing computer technology. This trade-off will always be necessary, but implicit modeling of some processes can reduce the computational load at run time so CPU cycles may be devoted to other areas of the model. Unfortunately some costly processes, such as intervisibility calculations, are even more expensive (in terms of storage) to model implicitly. This paper examines the potential of artificial neural networks to serve as efficient meta-models for line-of-sight determination.