A Network Representation of First-Order Logic That Uses Token Evolution for Inference.
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| Title: | A Network Representation of First-Order Logic That Uses Token Evolution for Inference. |
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| Authors: | HIDEAKI SUZUKI1 HSUZUKI@nict.go.jp, MIKIO YOSHIDA2 yos@bbr.jp, HIDEFUMI SAWAI1 SAWAI@nict.go.jp |
| Source: | Journal of Information Science & Engineering. May2014, Vol. 30 Issue 3, p669-689. 18p. |
| Subjects: | Horn clauses, Logic programming, Logic circuits, Computer networks, Numerical analysis, Stochastic convergence |
| Abstract: | A method to represent first-order predicate logic (Horn clause logic) by a data-flow network is presented. Like a data-flow computer for a von Neumann program, the proposed network explicitly represents the logical structure of a declarative program by unlabeled edges and operation nodes. In the deduction, the network first propagates symbolic tokens to create an expanded AND/OR network by the backward deduction, and then executes unification by a newly developed method to solve simultaneous equations buried in the network. The paper argues the soundness and completeness of the network in a conventional way, then explains how a kind of ambiguous solution is obtained by the newly developed method. Numerical experiments are also conducted with some data-flow networks, and the method's convergence ability and scaling property to larger problems are investigated. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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