IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Special Section on Foundations of Computer Science
Polynomial Time Inductive Inference of TTSP Graph Languages from Positive Data
Ryoji TAKAMIYusuke SUZUKITomoyuki UCHIDATakayoshi SHOUDAI
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2009 Volume E92.D Issue 2 Pages 181-190

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Abstract
Two-Terminal Series Parallel (TTSP, for short) graphs are used as data models in applications for electric networks and scheduling problems. We propose a TTSP term graph which is a TTSP graph having structured variables, that is, a graph pattern over a TTSP graph. Let${\\cal TG_{TTSP}}$ be the set of all TTSP term graphs whose variable labels are mutually distinct. For a TTSP term graph g in ${\\cal TG_{TTSP}}$, the TTSP graph language of g, denoted by L(g), is the set of all TTSP graphs obtained from g by substituting arbitrary TTSP graphs for all variables in g. Firstly, when a TTSP graph G and a TTSP term graph g are given as inputs, we present a polynomial time matching algorithm which decides whether or not L(g) contains G. The minimal language problem for the class ${\\cal L_{TTSP}}=\\{L(g) \\mid g\\in {\\cal TG_{TTSP}}\\}$ is, given a set S of TTSP graphs, to find a TTSP term graph g in ${\\cal TG_{TTSP}}$ such that L(g) is minimal among all TTSP graph languages which contain all TTSP graphs in S. Secondly, we give a polynomial time algorithm for solving the minimal language problem for ${\\cal L_{TTSP}}$. Finally, we show that ${\\cal L_{TTSP}}$ is polynomial time inductively inferable from positive data.
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© 2009 The Institute of Electronics, Information and Communication Engineers
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