01300nas a2200145   4500008004100000245007000041210006900111300001400180490000700194520084600201100001801047700002101065700002001086856004801106       2010                        eng d00aExtracting Reduced Logic Programs from Artificial Neural Networks0 aExtracting Reduced Logic Programs from Artificial Neural Network  a249–2660 v323 a<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 — where simple is being understood in some clearly defined and meaningful way.</p>
1 aLehmann, Jens1 aBader, Sebastian1 aHitzler, Pascal  uhttp://dx.doi.org/10.1007/s10489-008-0142-y