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We present a framework for a large-scale distributed eScience ArtificialIntelligence search. Our approach is generic and can be used for many differentproblems. Unlike many other approaches, we do not require dedicated machines,homogeneous infrastructure or the ability to communicate between nodes. We givespecial consideration to the robustness of the framework, minimising the lossof effort even after total loss of infrastructure, and allowing easyverification of every step of the distribution process. In contrast to mosteScience applications, the input data and specification of the problem is verysmall, being easily given in a paragraph of text. The unique challenges ourframework tackles are related to the combinatorial explosion of the space thatcontains the possible solutions and the robustness of long-runningcomputations. Not only is the time required to finish the computations unknown,but also the resource requirements may change during the course of thecomputation. We demonstrate the applicability of our framework by using it tosolve a challenging and hitherto open problem in computational mathematics. Theresults demonstrate that our approach easily scales to computations of a sizethat would have been impossible to tackle in practice just a decade ago.

A framework for large-scale distributed AI search across disconnected heterogeneous infrastructures