Once a solution used by just a few data scientists, model ensembles are now being used to solve more and more problems. That's due in part to the highly publicized Netflix prize and in part to the many tools that now make it easier for users to develop a solution containing multiple models.
In a model ensemble situation, every model is executed and the overall result or output is a combination of the partial results obtained from each model.
PMML is capable of representing not only model ensembles, but also composition, segmentation, and chaining. The same is true for ADAPA, which can consume PMML files containing multiple models.
For PMML Examples, including an example file for Random Forest Models, please refer to the DMG PMML Examples page.