The integration of connectionism and first-order knowledge representation and reasoning as a challenge for artificial intelligence

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The integration of connectionism and first-order knowledge representation and reasoning as a challenge for artificial intelligence (2006)

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by Sebastian Bader , Pascal Hitzler , Steffen Hölldobler

Venue:	In Proceedings of the Third International Conference on Information
Citations:	12 - 8 self

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@INPROCEEDINGS{Bader06theintegration,
    author = {Sebastian Bader and Pascal Hitzler and Steffen Hölldobler},
    title = {The integration of connectionism and first-order knowledge representation and reasoning as a challenge for artificial intelligence},
    booktitle = {In Proceedings of the Third International Conference on Information},
    year = {2006},
    pages = {22--33}
}

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Abstract

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems. 1

Keyphrases

artificial intelligence first-order knowledge representation connectionist system symbolic knowledge extraction algorithm symbolic knowledge representation intelligent system ultimate goal main obstacle first-order logic artificial neural network neural-symbolic system satisfactory answer robust neural networking machinery current state-of-the-art research

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