  We present a framework for a large-scale distributed eScience Artificial
Intelligence search. Our approach is generic and can be used for many different
problems. Unlike many other approaches, we do not require dedicated machines,
homogeneous infrastructure or the ability to communicate between nodes. We give
special consideration to the robustness of the framework, minimising the loss
of effort even after total loss of infrastructure, and allowing easy
verification of every step of the distribution process. In contrast to most
eScience applications, the input data and specification of the problem is very
small, being easily given in a paragraph of text. The unique challenges our
framework tackles are related to the combinatorial explosion of the space that
contains the possible solutions and the robustness of long-running
computations. Not only is the time required to finish the computations unknown,
but also the resource requirements may change during the course of the
computation. We demonstrate the applicability of our framework by using it to
solve a challenging and hitherto open problem in computational mathematics. The
results demonstrate that our approach easily scales to computations of a size
that would have been impossible to tackle in practice just a decade ago.
