Narrative Planning Model Acquisition from Text Summaries and Descriptions

AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the system-extracted domain models, in combination with significant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.

Factors affecting learning to write in first grade

Background. Written composition requires handwriting, spelling, and text-planning skills, all largely learned through school instruction. The rate at which students learn to compose text in their first months at school will depend, in part, on their literacy-related abilities at school start. These effects have not previously been explored.Aim. We aimed to establish the effects of various literacy-related abilities on the learning trajectory of first-grade students as they are taught to write.Sample. 179 Spanish first-grade students (94 female, mean age 6.1 years) writing 3515 textsMethod. Students were assessed at start-of-school for spelling, transcription fluency, letter knowledge, phonological awareness, handwriting accuracy, word reading, and non-verbal reasoning. They were then taught under a curriculum that included researcher-designed instruction in handwriting, spelling, and narrative planning. Students’ composition performance was probed at very regular intervals over the first 13 weeks of instruction.Results. Controlling for age, overall performance was predicted by spelling, transcription fluency, handwriting accuracy, word reading, and non-verbal reasoning. Most students showed rapid initial improvement, but then more gradual learning. Weak spellers showed weaker initial performance, but then improved steadily across the study period.Conclusion. Findings suggest the need to assess writing as a learned skill that develops differentially across students in response to specific instruction, rather than as a static ability.

High-Fidelity Simulated Players for Interactive Narrative Planning

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

Narrative Planning: Compilations to Classical Planning

A model of story generation recently proposed by Riedl and Young casts it as planning, with the additional condition that story characters behave intentionally. This means that characters have perceivable motivation for the actions they take. I show that this condition can be compiled away (in more ways than one) to produce a classical planning problem that can be solved by an off-the-shelf classical planner, more efficiently than by Riedl and Young's specialised planner.

Narrative Planning: Balancing Plot and Character

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors -- logical and aesthetic -- that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem -- to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL) planner -- that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.