A model of suspense for narrative generation

Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience’s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges’ normalised average scores correlate strongly with predicted values

A domain-independent model of suspense in narrative

Many computational models of narrative have focussed on the structure of the narrative world. Such models have been implemented in a wide variety of systems, often linked to characters’ goals and plans, where the goal of creating suspenseful stories is baked into the structure of each system. There is no portable, independently motivated idea of what makes a suspenseful story. Our approach is instead to take the phenomenon of suspense as the starting point. We extend an existing psychological model of narrative by Brewer and Lichtenstein (1982) which postulates suspense, curiosity and surprise as the fundamental elements of entertaining stories. We build a formal model of these phenomena using structures we call narrative threads. Narrative threads are a formal description of a reader’s expectations about what might happen next in a given story. Our model uses a measure for the imminence of the predicted conflict between narrative threads to create a suspense profile for a given story. We also identify two types of suspense: conflict-based and revelatory suspense. We tested the validity of our model by asking participants to give step- by-step self-reported suspense levels on reading online story variants. The results show that the normalised average scores of participants (N = 46) are in agreement with the values predicted by our model to a high level of statistical significance. Our model’s interface with storyworld knowledge is compatible with recent developments in automatic harvesting of world knowledge in the form of event chains such as Chambers and Jurafsky (2008). This means that it is in principle scalable. By disentangling suspense from specific narrative content and planning strategies, we arrive at a domain-independent model that can be reused within different narrative generation systems. We see our work as a signpost to encourage the further development of narrative models based on what we see as its fundamental ingredients

Predicting the effects of suspenseful outcome for automatic storytelling

Automatic story generation systems usually deliver suspense by including an adverse outcome in the narrative, in the assumption that the adversity will trigger a certain set of emotions that can be categorized as suspenseful. However, existing systems do not implement solutions relying on predictive models of the impact of the outcome on readers. A formulation of the emotional effects of the outcome would allow storytelling systems to perform a better measure of suspense and discriminate among potential outcomes based on the emotional impact. This paper reports on a computational model of the effect of different outcomes on the perceived suspense. A preliminary analysis to identify and evaluate the affective responses to a set of outcomes commonly used in suspense was carried out. Then, a study was run to quantify and compare suspense and affective responses evoked by the set of outcomes. Next, a predictive model relying on the analyzed data was computed, and an evolutionary algorithm for automatically choosing the best outcome was implemented. The system was tested against human subjects' reported suspense and electromyography responses to the addition of the generated outcomes to narrative passages. The results show a high correlation between the predicted impact of the computed outcome and the reported suspense

A Component-Based Architecture for Suspense Modelling

Suspense is a key narrative issue in terms of emotional gratification, influencing directly the way in which the audience experiences a story. Disciplines like psychology, neurology or e-learning study the suspense as the basis of useful techniques for the treatment of mental diseases or improving memory skills and the comprehension. In the field of creativity, it’s an essential cross strategy found in almost any book, film and video-game plots, regardless of technology and genre. With the objective of generating engaging stories, some automatic storytelling systems implement a suspense generation module. These systems are mainly based on narrative theories. However, we observe a lack of aspects from behavioral sciences, involving the study of empathy and emotional effect of scene objects in the audience. Generated plots with an adequate treatment of these features may involve benefits in areas as education and psychology. In this paper, we propose a component-based architectural model that firstly aims to identify and extract all these individual factors of the suspense from a scene; in a second step, the system calculates the level of suspense using a weighted corpus; in the last step, it alters those elements to increase or decrease the original suspense level and reassembles them in a new scene. Further, we discuss the model facing the development challenges and its practical implications.This work has been funded by the Andalusian Government under the University of Cadiz programme for Researching and Innovation in Education. This paper has been partially supported by the projects WHIM 611560 and PROSECCO 600653 funded by the European Commission, Framework Program 7, the ICT theme, and the Future and Emerging Technologies FET program.8 page

Narrative Generation in Entertainment: Using Artificial Intelligence Planning

From the field of artificial intelligence (AI) there is a growing stream of technology capable of being embedded in software that will reshape the way we interact with our environment in our everyday lives. This ‘AI software’ is often used to tackle more mundane tasks that are otherwise dangerous or meticulous for a human to accomplish. One particular area, explored in this paper, is for AI software to assist in supporting the enjoyable aspects of the lives of humans. Entertainment is one of these aspects, and often includes storytelling in some form no matter what the type of media, including television, films, video games, etc. This paper aims to explore the ability of AI software to automate the story-creation and story-telling process. This is part of the field of Automatic Narrative Generator (ANG), which aims to produce intuitive interfaces to support people (without any previous programming experience) to use tools to generate stories, based on their ideas of the kind of characters, intentions, events and spaces they want to be in the story. The paper includes details of such AI software created by the author that can be downloaded and used by the reader for this purpose. Applications of this kind of technology include the automatic generation of story lines for ‘soap operas’

Optimizing Player and Viewer Amusement in Suspense Video Games

Broadcast video games need to provide amusement to both players and audience. To achieve this, one of the most consumed genres is suspense, due to the psychological effects it has on both roles. Suspense is typically achieved in video games by controlling the amount of delivered information about the location of the threat. However, previous research suggests that players need more frequent information to reach similar amusement than viewers, even at the cost of jeopardizing viewers' engagement. In order to obtain models that maximize amusement for both interactive and passive audiences, we conducted an experiment in which a group of subjects played a suspenseful video game while another group watched it remotely. The subjects were asked to report their perceived suspense and amusement, and the data were used to obtain regression models for two common strategies to evoke suspense in video games: by alerting when the threat is approaching and by random circumstantial indications about the location of the threat. The results suggest that the optimal level is reached through randomly providing the minimal amount of information that still allows players to counteract the threat.We reckon that these results can be applied to a broad narrative media, beyond interactive games

Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc

The Ta-Da Series: presentation of a technique and its use in generating a series of surprising designs

Surprise is an emotion that is used very explicitly in personal interactions and in narrative media, yet it is not used in the same way within design. This case study presents a technique devised and used to apply the results of theoretical research on surprise to the creation of a series of surprising objects. The designs in this series are very different in the way they function, yet they are derived from the same technique, based on cultural expectations, gut reactions and pleasant surprise. To begin with, the design process involved studying what is expected of objects, and identifying what the main characteristics of a specific category of objects are. What do we expect when we approach a lamp? And in particular, are there any signs which we can use to reinforce these expectations? The second step is to find the opposite of those characteristics and turn them into design concepts. In this case a lamp needs to make light in order to be a lamp, so its main connotation cannot be opposed. But there are other connotations that are not necessarily intrinsic in lamps but which we all tend to associate with lamps, and those are connotations and those are connotations about breakable materials and fragility. The design therefore plays with these expectations by creating a lamp that at first sight has some connotations of a typology of lamp that is both common and extremely breakable; in this way it reinforces the feeling of fragility. But the lamp itself is made of rubber, so if it fell it wouldn’t break but bounce. In addition to this, the technique uses inbuilt gut reactions and fears to reinforce the surprising effect. The lamp only turns on when it is placed on the edge of the table; in this way the lamp will always be in a precarious position, not only reinforcing the feeling of instability, but playing with the user’s gut reactions: though the owner knows that the lamp will not break, it is hard to shed the ingrained reaction of wanting to move it to the middle of the table. By using these gut reactions, the lamp creates a playful sense of suspense, and pleasant surprise when one discovers, or remembers, that the lamp is made of rubber and it is meant to fall. This same technique is applied to three designs, the On-Edge Lamp, the (Un-) Stable Stool and the Impolite Coffee Tables. These three designs will be presented and the differences and similarities between the designs will be outlined

The Ta-Da Series: A Technique for Generating Surprising Designs Based on Opposites and Gut Reactions

Chapter in an edited collection, originally presented at the 5th international Design and Emotion Conference in Gothenburg, Sweden