Inproceedings906
Extracting Reduced Logic Programs from Artificial Neural Networks
Extracting Reduced Logic Programs from Artificial Neural Networks
Published: 2005
August
Herausgeber: Artur Garcez, Pascal Hitzler, and Jeff Ellman
Buchtitel: Proceedings of the IJCAI-05 Workshop on Neural-Symbolic Learning and Reasoning, NeSy
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Kurzfassung
Artificial neural networks can be trained to perform
excellently in many application areas. While
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.
Download: Media:2005_906_Lehmann_Extracting_Redu_1.pdf
Neuro-symbolische Integration, Logikprogrammierung, Künstliche Intelligenz