Scenario management and automated scenario generation

McKeever, William; Gilmour, Duane A.; Lehman, Lynn A.; Stirtzinger, Anthony; Krause, Lee

doi:10.1117/12.672883

Cited by 6 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automated story-based scenario generation, or in a broader sense, automated story generation has not only drawn significant attention from researchers within the artificial intelligence (AI) and linguistic research communities, but also in the entertainment industry and military domain [17], [30]. Significant work has been carried out to automate the story generation process using various techniques, such as multi-agent based planning [19], [21], [30], ontology based reasoning [3], [12], [23], and problem solving with case based reasoning [8], [10].…”

Section: Introductionmentioning

confidence: 99%

Evolving stories: Grammar evolution for automatic plot generation

Bui

Abbbass

Bender

2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

In this paper, we propose a computational framework for automated story-based scenario generation. Under this framework, a regular grammar is developed to model various causal relationships inside a given story world. The grammar is then evolved using evolutionary computation techniques to generate novel story plots, i.e. story-based scenarios. To evaluate these newly generated scenarios, a human-in-the-loop model is used. An experimental study was carried out, in which the proposed framework was used to create novel plots based on the famous Little Red Riding Hood fairy tale. The experimental study demonstrated that evolutionary computation can potentially contribute significantly to story generations. Some challenges were identified including the difficulty to quantify such subjective measures as plot interestingness and creativity.

show abstract

Section: Introductionmentioning

confidence: 99%

Evolving stories: Grammar evolution for automatic plot generation

Bui

Abbbass

Bender

2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…The area of automatic generation of stories and scenarios has drawn significant attention from the research community due to its importance in domains such as strategic planning, cyber security and the entertainment industry [22], [26]. Although a notable progress has been made [3], [5], [7], [10], [12], [17], [23], [35], generating stories and story-like scenarios remains a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

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

Bui

Bender

Abbass

2012

2012 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

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.

show abstract