Graph Grammar Induction via Evolutionary Computation.

Augmented Graph Grammars provide a robust formalism for representing and evaluating graph structures. With the advent of robust graph libraries such as AGG, it has become possible to use graph grammars to analyze realistic data. Prior studies have shown that graph rules can be used to evaluate student work and to identify empirically-valid substructures using hand-authored rules. In this paper we describe proposed work on the automatic induction of graph grammars for student data using evolutionary computation via the pyEC system.