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 algorithmthe 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.

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Storytelling, in oral, visual, or written forms, plays a central role in various types of media, including novels, movies, television, and theater. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -the ability to organize experience into narrative form -is central to the cognitive processes employed across a range of experiences, from entertainment to active learning. It follows that computational systems possessing narrative intelligence may be able to interact with human users naturally because they understand collaborative contexts as emerging narrative and are able to express themselves by telling stories. A number of narrative intelligence tasks have been studied from a computational perspective including story understanding, story generation, and commonsense reasoning.One of the most compelling applications of narrative intelligence is the prospect of interactive narrative. Interactive narrative is a form of digital interactive experience in which users create or influence a dramatic storyline through actions, either by assuming the role of a character in a fictional virtual world, issuing commands to computer-controlled characters, or directly manipulating the fictional world state. It is most often considered as a form of interactive entertainment but can also be used for serious applications such as education and training. The most common form of interactive narrative involves the user taking on the role of the protagonist in an unfolding storyline.

Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at https://github.com/rajammanabrolu/KG-DQN.

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Language-modeling-based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socioorganizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.CCS Concepts: • Human-centered computing → Scenario-based design; Empirical studies in HCI; HCI theory, concepts and models; Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Artificial intelligence.

Narrative is an important part of the way we interact with and make sense of the world. Interactive narrative systems tell stories in a virtual world in which the user is an interactive participant.Since the behaviors the user performs in the virtual world can affect the way in which a storyline unfolds, interactive narrative systems often use a branching story structure where non-interactive story presentations are interleaved with user decision points. An alternative approach -narrative mediation -represents story as a linear progression of events with anticipated user actions and system-controlled agent actions together in a partially-ordered plan. For every possible way the user can violate the story plan, an alternative story plan is generated. If narrative mediation is powerful enough to express the same interactive stories as systems that use branching story structures, then linear narrative generation techniques can be applied to interactive narrative generation with the use of narrative mediation. This paper sketches out a proof that narrative mediation is at least as powerful as acyclic branching story structures.