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Aktuelle Version vom 16. Oktober 2009, 17:24 Uhr
Integrating First Order Logic Programs and Connectionist Systems - A Constructive Approach
Integrating First Order Logic Programs and Connectionist Systems - A Constructive Approach
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
Referierte Veröffentlichung
BibTeX
Kurzfassung
Significant advances have recently been made concerning
the integration of symbolic knowledge representation
with artificial neural networks (also
called connectionist systems). However, while the
integration with propositional paradigms has resulted
in applicable systems, the case of first-order
knowledge representation has so far hardly proceeded
beyond theoretical studies which prove the
existence of connectionist systems for approximating
first-order logic programs up to any chosen precision.
Advances were hindered severely by the
lack of concrete algorithms for obtaining the approximating
networks which were known to exist:
the corresponding proofs are not constructive
in that they do not yield concrete methods for
building the systems. In this paper, we will make
the required advance and show how to obtain the
structure and the parameters for different kinds of
connectionist systems approximating covered logic
programs.
Download: Media:2005_905_Bader_Integrating_Fir_1.pdf
Neuro-symbolische Integration, Logikprogrammierung, Künstliche Intelligenz