Using a simple interface, users can specify arbitrary known constraint functions on the parameter space, which results in portions of the space being omitted from consideration by planners. We also supply convenient ways to specify commonly occurring known constraint types, including compositional (simplex), 65 permutation (ordering), pending experiments, or process-constrained batches. The following code snippet shows the instantiation of the GPPlanner with a user-defined constraint function for the Dejong surface, f : X ∈ R 2 → R. Constraint functions are Python callables which return a boolean value, True (False) for feasible (infeasible) parameters, and are passed to the constructor of GPPlanner as a list using the known constraints argument. Next, we show an example of a special known constraint case built into Atlas: compositional or simplex constraints.