<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tarek R. Besold</style></author><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Sebastian Bader</style></author><author><style face="normal" font="default" size="100%">Howard Bowman</style></author><author><style face="normal" font="default" size="100%">Pedro Domingos</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kai-Uwe Kühnberger</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author><author><style face="normal" font="default" size="100%">Daniel Lowd</style></author><author><style face="normal" font="default" size="100%">Priscila Machado Vieira Lima</style></author><author><style face="normal" font="default" size="100%">Leo de Penning</style></author><author><style face="normal" font="default" size="100%">Gadi Pinkas</style></author><author><style face="normal" font="default" size="100%">Hoifung Poon</style></author><author><style face="normal" font="default" size="100%">Gerson Zaverucha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-Symbolic Learning and Reasoning: A Survey and Interpretation</style></title><secondary-title><style face="normal" font="default" size="100%">Neuro-Symbolic Artificial Intelligence: The State of the Art</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steffen Hölldobler</style></author><author><style face="normal" font="default" size="100%">Sebastian Bader</style></author><author><style face="normal" font="default" size="100%">Bertram Fronhöfer</style></author><author><style face="normal" font="default" size="100%">Ursula Hans</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Tobias Pietzsch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Logik und Logikprogrammierung Band 2: Aufgaben und Lösungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Synchron Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jens Lehmann</style></author><author><style face="normal" font="default" size="100%">Sebastian Bader</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extracting Reduced Logic Programs from Artificial Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10489-008-0142-y</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">249–266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Artificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible — where simple is being understood in some clearly defined and meaningful way.&lt;/p&gt;
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