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Towards an Atlas of Computational Learning Theory Kötzing, Timo Schirneck, Martin computational learning language learning partially set-driven learning strongly monotone learning A major part of our knowledge about Computational Learning stems from comparisons of the learning power of different learning criteria. These comparisons inform about trade-offs between learning restrictions and, more generally, learning settings; furthermore, they inform about what restrictions can be observed without losing learning power. With this paper we propose that one main focus of future research in Computational Learning should be on a structured approach to determine the relations of different learning criteria. In particular, we propose that, for small sets of learning criteria, all pairwise relations should be determined; these relations can then be easily depicted as a map, a diagram detailing the relations. Once we have maps for many relevant sets of learning criteria, the collection of these maps is an Atlas of Computational Learning Theory, informing at a glance about the landscape of computational learning just as a geographical atlas informs about the earth. In this paper we work toward this goal by providing three example maps, one pertaining to partially set-driven learning, and two pertaining to strongly monotone learning. These maps can serve as blueprints for future maps of similar base structure. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Timo Kötzing and Martin Schirneck 2016 Is Part Of LIPIcs, Volume 47, 33rd Symposium on Theoretical Aspects of Computer Science (STACS 2016) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.STACS.2016.47 urn:nbn:de:0030-drops-57483 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2016.47 eng https://creativecommons.org/licenses/by/3.0/legalcode