Abstract
This chapter describes one method for addressing knowledge representation issues that arise when a connectionist system replicates a standard symbolic style of inference for general inference. Symbolic rules are encoded into the networks, called structured predicate networks (SPN) using neuron-like elements. Knowledge-representation issues such as unification and consistency checking between two groups of unifying arguments arise when a chain of inference is formed over the networks encoding special type of symbol rules. These issues are addressed by connectionist sub-mechanisms embedded into the networks. As a result, the proposed SPN architecture is able to translate a significant subset of first-order Horn Clause expressions into a connectionist representation that may be executed very efficiently.
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© 2000 Springer-Verlag Berlin Heidelberg
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Park, N.S. (2000). Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_6
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DOI: https://doi.org/10.1007/10719871_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67305-7
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