Abstract
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.
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Wang, K., Bui, V.Q., Abbass, H.A. (2010). Evolving Stories: Tree Adjoining Grammar Guided Genetic Programming for Complex Plot Generation. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_14
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DOI: https://doi.org/10.1007/978-3-642-17298-4_14
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