- author = "Linting Xue and Collin F. Lynch and Min Chi",
- title = "Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection",
- booktitle = "Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017)",
- year = "2017",
- editor = "Xiangen Hu and Tiffany Barnes and Arnon Hershkovitz and Luc Paquette",
- pages = "296--301",
- address = "Wuhan China",
- month = "25-28 " # jun,
- publisher = "International Educational Data Mining Society",
- keywords = "genetic algorithms, genetic programming, Heterogeneous Rules, Augmented Graph Grammars, Argument Diagrams, Evolutionary Computation, Novelty selection",
-
URL = "
http://educationaldatamining.org/EDM2017/proc_files/papers/paper_107.pdf",
- size = "6 pages",
- abstract = "Augmented Graph Grammars are a graph-based rule formalism that supports rich relational structures. They can be used to represent complex social networks, chemical structures, and student-produced argument diagrams for automated analysis or grading. In prior work we have shown that Evolutionary Computation (EC) can be applied to induce empirically-valid grammars for student-produced argument diagrams based upon fitness selection. However this research has shown that while the traditional EC algorithm does converge to an optimal fitness, premature convergence can lead to it getting stuck in local maxima, which may lead to undiscovered rules. In this work, we augmented the standard EC algorithm to induce more heterogeneous Augmented Graph Grammars by replacing the fitness selection with a novelty-based selection mechanism every ten generations. Our results show that this novelty selection increases the diversity of the population and produces better, and more heterogeneous, grammars.",
- cv-category = "Peer-Reviewed Conference Paper",
-
notes = "Linting Xue, PhD NCSU.EDU 2020
?
http://educationaldatamining.org/EDM2017/",
