Evolving Stories: Tree Adjoining Grammar Guided Genetic Programming for Complex Plot Generation

Wang, Kun; Bui, Vinh; Abbass, Hussein A.

doi:10.1007/978-3-642-17298-4_14

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…The literature shows several examples of this family of generation techniques, and implementations of this method are common in Computational Creativity and Computational Narrative. Metrics for evaluating the effect of stories include coherence, causality, or character affinity [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

2021

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The perception of suspense in stories is affected not only by general literary aspects like narrative structure and linguistic features, but also by anticipation and evocation of feelings like aversion, disgust or empathy. As such, it is possible to alter the feeling of suspense by modifying components of a story that convey these feelings to the audience. Based on a previous straightforward model of suspense adaptation, this paper describes the design, implementation and evaluation of a computational system that adapts narrative scenes for conveying a specific user-defined amount of suspense. The system is designed to address the impact of different types of emotional components on the reader. The relative weighted suspense of these components is computed with a regression model based on a sentiment analysis tool, and used as a fitness function in an evolutionary algorithm. This new function is able to identify the different weights on the prediction of suspense in aspects like outcome, decorative elements, or threat's appearance. The results indicate that this approach represents a significant improvement over the previous existing approach.

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Section: Introductionmentioning

confidence: 99%

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

2021

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“…The approach can be supplemented with search techniques, e.g. grammar evolution as in [7], [34] to generate useful scenarios.…”

Section: Introductionmentioning

confidence: 99%

An expressive GL-2 grammar for representing story-like scenarios

Bui

Bender

Abbass

2012

2012 IEEE Congress on Evolutionary Computation

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This paper extends our previous work on evolving stories towards a computational platform for automatic scenario generation. In particular, we address a shortcoming of our earlier work relating to scenario representation: the regular story plot grammar. The use of this grammar, which only captures causal relationships between plot elements from the point of view of a single story character, resulted in the generation of stories that are always associated with one main character only, limiting the scalability of the approach. In addition, the regular grammar employed is not suitable for representing practical scenarios since a practical scenario may not require a character at all. To overcome these problems, we propose a new approach to scenario representation. Firstly, we introduce a set of scenario building blocks based on narrative theory. As a result, a scenario can be represented as a network of these building blocks that captures various relationships in the scenario. The task of generating scenarios is then transformed to the task of generating networks of these building blocks. Secondly, we develop two network representation languages extending Boers' GL-2 graph representation systems to describe networks with edges of more than one type and edges between two groups of nodes. Thirdly, a set of two context-free grammars is introduced to generate sentences, i.e. scenarios in these languages. Finally, we verify our approach to strategic scenario generation by employing an interactive evolution framework, which shows the proposed scenario representation scheme can facilitate the generation of coherent and novel story-like scenarios.

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Evolving Narrations of Strategic Defence and Security Scenarios for Computational Scenario Planning

Wang

Petraki

Abbass

2015

Recent Advances in Computational Intelligence in Defense and Security

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Evolving Stories: Tree Adjoining Grammar Guided Genetic Programming for Complex Plot Generation

Cited by 7 publications

References 14 publications

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

An expressive GL-2 grammar for representing story-like scenarios

Evolving Narrations of Strategic Defence and Security Scenarios for Computational Scenario Planning

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