Using the tree representation of terms to recognize matching with neural networks

✍ Scribed by Carlos Mareco; Alberto Paccanaro

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 682 KB
• Volume: 30
• Category: Article
• ISSN: 0362-546X
• DOI: 10.1016/s0362-546x(97)00346-5

✦ Synopsis

In this paper we investigate the use of neural networks to recognize the matching relation among First Order Logic (FOL) terms. Given n+l terms {ti, T with i=l, . . . . n}, the network should identify those trs which are matched by T or one of its subterms. One of the main issues is how to properly represent the terms to be given as inputs to the networks. A way of mapping the tree structure of the terms into a form suitable to be input to a neural network is proposed. Results of experiments are presented in which some of the networks were able to recognize the matching relation in up to almost 95% of the cases. Finally "matchers" were built to solve sets of matching problems of the kind that ofbm appear in rewriting based automated theorem proving. The results show a great reduction in the number of trials needed for solving such problems when using neural networks.