when quoting this document, please refer to the following
DOI:
URN: urn:nbn:de:0030-drops-17703
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### Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence

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### Abstract

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.

### BibTeX - Entry

@InProceedings{valiant:LIPIcs:2008:1770,
author =	{Leslie G Valiant},
title =	{{Knowledge Infusion: In Pursuit of Robustness in Artificial Intelligence}},
booktitle =	{IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science},
pages =	{415--422},
series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN =	{978-3-939897-08-8},
ISSN =	{1868-8969},
year =	{2008},
volume =	{2},
editor =	{Ramesh Hariharan and Madhavan Mukund and V Vinay},
publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},