378 research outputs found
The Virtual Storyteller: story generation by simulation
The Virtual Storyteller is a multi-agent framework that generates stories based on a concept called emergent narrative. In this paper, we describe the motivation and approach of the Virtual Storyteller, and give an overview of the computational processes involved in the story generation process. We also discuss some of the challenges posed by our chosen approach
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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 charactersā 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 readerās 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 modelās 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
A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling
Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesopās Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Talesā automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)
This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory
2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP
2015) at the China National Convention Center in Beijing, on July 31st 2015.
Narratives are at the heart of information sharing. Ever since people began to share their experiences,
they have connected them to form narratives. The study od storytelling and the field of literary theory
called narratology have developed complex frameworks and models related to various aspects of
narrative such as plots structures, narrative embeddings, charactersā perspectives, reader response, point
of view, narrative voice, narrative goals, and many others. These notions from narratology have been
applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g.
Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an
autonomous field of study and research. Narrative has been the focus of a number of workshops and
conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of
Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML
by Mani (2013)).
The workshop aimed at bringing together researchers from different communities working on
representing and extracting narrative structures in news, a text genre which is highly used in NLP
but which has received little attention with respect to narrative structure, representation and analysis.
Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic
extraction of events from single documents and work towards extracting story structures from multiple
documents, while these documents are published over time as news streams. Policy makers, NGOs,
information specialists (such as journalists and librarians) and others are increasingly in need of tools
that support them in finding salient stories in large amounts of information to more effectively implement
policies, monitor actions of ābig playersā in the society and check facts. Their tasks often revolve around
reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g.
hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections
of relevant information but also projections to the future. They form a valuable potential for exploiting
news data in an innovative way.JRC.G.2-Global security and crisis managemen
Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey
Storytelling and narrative are fundamental to human experience, intertwined
with our social and cultural engagement. As such, researchers have long
attempted to create systems that can generate stories automatically. In recent
years, powered by deep learning and massive data resources, automatic story
generation has shown significant advances. However, considerable challenges,
like the need for global coherence in generated stories, still hamper
generative models from reaching the same storytelling ability as human
narrators. To tackle these challenges, many studies seek to inject structured
knowledge into the generation process, which is referred to as structure
knowledge-enhanced story generation. Incorporating external knowledge can
enhance the logical coherence among story events, achieve better knowledge
grounding, and alleviate over-generalization and repetition problems in
stories. This survey provides the latest and comprehensive review of this
research field: (i) we present a systematical taxonomy regarding how existing
methods integrate structured knowledge into story generation; (ii) we summarize
involved story corpora, structured knowledge datasets, and evaluation metrics;
(iii) we give multidimensional insights into the challenges of
knowledge-enhanced story generation and cast light on promising directions for
future study
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