@inbook {745,
	title = {Neural-Symbolic Learning and Reasoning: A Survey and Interpretation},
	booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art},
	year = {2022},
	publisher = {IOS Press},
	organization = {IOS Press},
	address = {Amsterdam},
	author = {Tarek R. Besold and Artur S. d{\textquoteright}Avila Garcez and Sebastian Bader and Howard Bowman and Pedro Domingos and Pascal Hitzler and Kai-Uwe K{\"u}hnberger and Lu{\'\i}s C. Lamb and Daniel Lowd and Priscila Machado Vieira Lima and Leo de Penning and Gadi Pinkas and Hoifung Poon and Gerson Zaverucha}
}
@book {291,
	title = {Logik und Logikprogrammierung Band 2: Aufgaben und L{\"o}sungen},
	year = {2011},
	publisher = {Synchron Verlag},
	organization = {Synchron Verlag},
	address = {Heidelberg, Germany},
	issn = {978-3-935025-85-0},
	author = {Steffen H{\"o}lldobler and Sebastian Bader and Bertram Fronh{\"o}fer and Ursula Hans and Pascal Hitzler and Markus Kr{\"o}tzsch and Tobias Pietzsch}
}
@article {81,
	title = {Extracting Reduced Logic Programs from Artificial Neural Networks},
	journal = {Applied Intelligence},
	volume = {32},
	year = {2010},
	pages = {249{\textendash}266},
	abstract = {<p class="rtejustify">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 {\textemdash} where simple is being understood in some clearly defined and meaningful way.</p>
},
	doi = {10.1007/s10489-008-0142-y},
	url = {http://dx.doi.org/10.1007/s10489-008-0142-y},
	author = {Jens Lehmann and Sebastian Bader and Pascal Hitzler}
}
