The HR3 Discovery System: Design Decisions and Implementation Details

Automated Theory Formation is a hybrid AI technique which has been implemented in two scientific discovery systems, HR1 and HR2, both of which have been used successfully in various applications. We describe here the latest iteration in the HR series, in terms of the lessons learned from the successes and failures of the previous versions, and how these lessons have informed our design choices and the implementation details of the new version. We also present two case studies: a synthetic domain mirroring an aspect of medical diagnosis, and invariant discovery in formal methods. In each case, we compare HR3 with HR2 to highlight various improvements in the new version.