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Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence Valiant, Leslie G Artificial intelligence knowledge acquisition knowledge representation,learning reasoning robustness Endowing computers with the ability to apply commonsense knowledge with human-level performance is a primary challenge for computer science, comparable in importance to past great challenges in other fields of science such as the sequencing of the human genome. The right approach to this problem is still under debate. Here we shall discuss and attempt to justify one approach, that of {\it knowledge infusion}. This approach is based on the view that the fundamental objective that needs to be achieved is {\it robustness} in the following sense: a framework is needed in which a computer system can represent pieces of knowledge about the world, each piece having some uncertainty, and the interactions among the pieces having even more uncertainty, such that the system can nevertheless reason from these pieces so that the uncertainties in its conclusions are at least controlled. In knowledge infusion rules are learned from the world in a principled way so that subsequent reasoning using these rules will also be principled, and subject only to errors that can be bounded in terms of the inverse of the effort invested in the learning process. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Leslie G Valiant 2008 Is Part Of LIPIcs, Volume 2, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (2008) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FSTTCS.2008.1770 urn:nbn:de:0030-drops-17703 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2008.1770 eng https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode