Data preparation for inductive learning in robotics

dc.contributor.authorRieger, Ankede
dc.date.accessioned2004-12-06T12:53:39Z
dc.date.available2004-12-06T12:53:39Z
dc.date.created1995de
dc.date.issued1999-10-29de
dc.description.abstractThe application of logic-based learning algorithms in real-world domains, such as robotics, requires extensive data engineering, including the transformation of numerical tabular representations of real-world data to logic-based representations, feature and concept selection, the generation of the respective descriptions, and the composition of training and test sets, which meet the requirements of the respective learning algorithms. We are developing a tool, which supports a user of inductive logic-based algorithms with handling these tasks. The tool is developed in the context of a robot navigation domain, in which different logic-based algorithms are applied to learn operational concepts. The paper is written in English.en
dc.format.extent191441 bytes
dc.format.extent725693 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.issn0943-4135de
dc.identifier.urihttp://hdl.handle.net/2003/2591
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5095
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesForschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 19de
dc.subject.ddc004de
dc.titleData preparation for inductive learning in roboticsen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

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