In this paper, we develop a tree adjoining grammar (TAG) to capture semantics of a story with long-distance causal dependency, and present a computational framework for story plot generation. Under this framework, TAG is derived and a story plot is represented by a derivation tree of TAG. The generated plots are then evolved using grammar guided genetic programming (GGGP) to generate creative, interesting and complex story plots. To evaluate these newly generated plots, a human-in-the-loop approach is used. An experimental study was carried out, in which this framework was used to produce creative, interesting and complex plots from a predesigned fabula based on a story known as "The magpie and the water bottle". The experimental study demonstrated that TAG and GGGP can potentially contribute significantly to complex automatic story plot generation. © 2010 Springer-Verlag.
CITATION STYLE
Wang, K., Bui, V. Q., & Abbass, H. A. (2010). Evolving stories: Tree adjoining grammar guided genetic programming for complex plot generation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6457 LNCS, pp. 135–145). https://doi.org/10.1007/978-3-642-17298-4_14
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