language: en

A Neural Net for Determining Structural Similarity of Recursive Programs

Kristina Schadler, Ute Schmid, Hendrik Lubben, Bernd Machenschalk
Research Group "Methods of Artificial Intelligence" Institute for Applied Computer Science, Technische Universitat Berlin
email: schaedle,schmid,compuman,herold@cs.tu-berlin.de

Abstract. In this work it will be shown, how the comparison of recursive program schemata (RPS) can be reduced to an only slightly modified type of the search for maximal isomorphic subgraphs by interpreting the RPS as directed, cyclic, labelled graphs. The quality of the mapping of two RPS can be used as a quantitative measure for similarity of RPS among each other. The simultaneously calculated graph morphism can serve as a basis for the detection of analogies between the RPS. A special neural net, developed for the solution of general graph-matching problems, realizes the search for a sensible and as comprising as possible mapping between two RPS. It is shown, which special properties of RPS have to be accounted for in the modeling and how these can be incorporated into the algorithms when using a special neural approach for the solution.

1. Structural similarities in case-based reason