Acquiring Planning Models from Narrative Synopses

  • Thomas Hayton

Student thesis: Doctoral Thesis

Abstract

The creation of planning domain models for automated planning is challenging,
especially when non-technical domain experts are required for the creation of content. This is particularly true for the creation of domain models for Interactive Storytelling systems and games. AI planning can be used for the task of narrative generation and this could be utilised in such systems. Therefore a tool supported approach to the creation of narrative planning models that alleviates the requirement of specific domain modelling expertise and automates parts of the process would make narrative generation a more accessible technology in this
context.
The aim of this thesis was to develop a semi-automated approach to the creation of narrative planning domain models that automates the process of domain modelling and is accessible to non-technical authors. The approach taken aimed to use narrative synopses as an input for which a planning domain model can be acquired from.
The contribution of this thesis is a novel approach for the acquisition of planning domain models from narrative synopses. The presented approach extracts the required planning information that is described by an input synopsis and from this automatically constructs a planning domain model that is representative of this information. Automated methods have been developed for the extraction of planning information that utilise having an author “in the loop” and exploit the contextual information available. A method for the automated construction of a planning domain model has been presented that is capable of reproducing
the original input. This acquired planning model can then be generalised by an author using the default narrative control mechanisms that the model provides to produce a model capable of generating new story variants. The approach was implemented in a prototype system and evaluated to demonstrate the effectiveness of the approach.
Date of Award1 Dec 2019
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorJulie Porteous (Supervisor)

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