D. Opitz & J. Shavlik (1995).
Dynamically Adding Symbolically Meaningful Nodes to Knowledge-Based Neural Networks. Knowledge-Based Systems, 8, pp. 301-311.
This publication is available in PDF and available in postscript.
Traditional connectionist theory-refinement systems map the dependencies of a domain-specific rule base into a neural network, and then refine this network using neural learning techniques. Most of these systems, however, lack the ability to refine their network's topology and are thus unable to add new rules to the (reformulated) rule base. Therefore, on domain theories that are lacking rules, generalization is poor, and training can corrupt the original rules, even those that were initially correct. We present TopGen, an extension to the KBANN algorithm, that heuristically searches for possible expansions to KBANN's network. TopGen does this by dynamically adding hidden nodes to the neural representation of the domain theory, in a manner analogous to adding rules and conjuncts to the symbolic rule base. Experiments indicate that our method is able to heuristically find effective places to add nodes to the knowledge bases of four real-world problems, as well as an artificial chess domain. The experiments also verify that new nodes must be added in an intelligent manner. Our algorithm showed statistically significant improvements over KBANN in all five domains.
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