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Computing first-order logic programs by fibring artificial neural networks (2005)
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| Venue: | Proceedings of the Eighteenth International Florida Artificial Intelligence Research Symposium Conference |
| Citations: | 6 - 3 self |
BibTeX
@INPROCEEDINGS{Bader05computingfirst-order,
author = {Sebastian Bader and Technische Universität Dresden and Artur S. D’avila Garcezy and Pascal Hitzlerz},
title = {Computing first-order logic programs by fibring artificial neural networks},
booktitle = {Proceedings of the Eighteenth International Florida Artificial Intelligence Research Symposium Conference},
year = {2005},
pages = {314--319},
publisher = {AAAI Press}
}
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Abstract
The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a very chal-lenging area of research. The overall aim is to merge these two very different major approaches to intelli-gent systems engineering while retaining their respec-tive strengths. For symbolic paradigms that use the syn-tax of some first-order language this appears to be par-ticularly difficult. In this paper, we will extend on an idea proposed by Garcez and Gabbay (2004) and show how first-order logic programs can be represented by fibred neural networks. The idea is to use a neural net-work to iterate a global counter n. For each clause Ci in the logic program, this counter is combined (fibred) with another neural network, which determines whether Ci outputs an atom of level n for a given interpreta-tion I. As a result, the fibred network approximates the single-step operator TP of the logic program, thus cap-turing the semantics of the program.
Keyphrases
first-order logic program artificial neural network logic program single-step operator tp neural-network-based artificial intelligence paradigm different major approach neural net-work overall aim clause ci fibred neural network global counter intelli-gent system engineering respec-tive strength symbolic paradigm first-order language chal-lenging area fibred network neural network
