A Cognitive Approach to Narrative Planning with Believable Characters

In this work, we address the question of generating understandable narratives using a cognitive approach. The requirements of cognitive plausibility are presented. Then an abduction-based cognitive model of the human deliberative reasoning ability is presented. We believe that implementing such a procedure in a narrative context to generate plans would increase the chances that the characters will be perceived as believable. Our suggestion is that the use of a deliberative reasoning procedure can be used as a basis of several strategies to generate interesting stories

Contesting the Commemorative Narrative: Planning for Richmond’s Cultural Landscape

Abstract: New Orleans, Baltimore, and Charlottesville are reevaluating the presence of Confederate statues in their built environment. Known as the Capital of the Confederacy, Richmond’s cultural landscape is visible through the connection of two historical spaces, Monument Avenue and Shockoe Bottom. Both serve as a powerful case study for how the commemorative narrative of these spaces is contested today and how barriers that exist influence urban planning processes and outcomes

Automated Action Model Acquisition from Narrative Texts

Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.Comment: 10 pages, 3 figure

Don\u27t Give Me That Story! -- A Human-Centered Framework for Usable Narrative Planning

Interactive or branching stories are engaging and can be embedded into digital systems for a variety of purposes, but their size and complexity makes it difficult and time-consuming for humans to author them. Narrative planning algorithms can automatically generate large branching stories with guaranteed causal consistency, using a hand-authored library of story content pieces. The usability of such a system depends on both the quality of the narrative model upon which it is built and the ability of the user to create the story content library. Current narrative planning algorithms use either a limited or no model of character belief, which typically leads to undesireable stories and difficult domain authoring challenges. Domain authoring is further complicated by a lack of intelligent tools for summarizing the content that a domain can produce so that its author can effectively evaluate it. In this work I extend a prior narrative planning framework to model deeply nested character beliefs, thus avoiding common character omniscience problems without overburdening the domain author. Human subjects evaluations demonstrate that the belief model tracks nested beliefs correctly, and that it improves overall character believability in solution spaces over previous models. This model makes domain authoring more intuitive, but also adds complexity to the story generation algorithm, making the planner\u27s output even harder for the author to predict. As a step toward more intelligent domain authoring tools, I present a novel method for measuring story similarity by encoding important story information into a fixed-length numeric vector. This enables automatic clustering of stories based on their semantic similarity, facilitating high-level communication of large story spaces. I compare the story similarity metric to assessments made by humans, and find the metric to be highly accurate in judging how similar two stories are to each other. I then demonstrate its use in clustering solution spaces, and evaluate two strategies for summarizing the content of the resulting clusters. I find both techniques to be more effective than a control in communicating large story spaces to humans. These contributions together advance the usability of narrative planning algorithms by improving their underlying narrative model and providing a basis for more intelligent domain authoring tools

An English Language Learner Co-Teaching Narrative: Planning, Models, and Relationships

This paper explores the experiences of an English Language Learners (ELL) teacher in a co-teaching relationship. This paper explains the difficulties that exist in the implementation of the co-teaching model, as well as the struggle to create parity in a co-teaching partnership. The existing research presents co-planning, implementing the co-teaching models in the classroom, and creating parity among the co-teaching pair as three important factors in a successful co-teaching model. A contributing factor to the success of both the co-teaching relationship and the implementation of this model in the classroom comes from the support of administration, the school, and the district at large. This paper explains the experience of five ELL co-teachers, their input as to how co-teaching can yet be improved, and their ideal co-teaching scenarios

Personality and Emotion for Virtual Characters in Strong-Story Narrative Planning

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. The interaction of rich virtual characters can produce interesting narratives and enhance user experience in virtual environments. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. I demonstrate the value of the strong-story properties of the model by generating story conflicts intelligently. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts using certain behavior types. In doing so, the author can encourage the player to adopt and exhibit those behaviors. I conduct multiple human subject and case studies to evaluate these models and show that they enable generating a larger number of stories and character behavior that is preferred and more believable to a human audience

A Planning-based Approach for Music Composition

. Automatic music composition is a fascinating field within computational creativity. While different Artificial Intelligence techniques have been used for tackling this task, Planning – an approach for solving complex combinatorial problems which can count on a large number of high-performance systems and an expressive language for describing problems – has never been exploited. In this paper, we propose two different techniques that rely on automated planning for generating musical structures. The structures are then filled from the bottom with “raw” musical materials, and turned into melodies. Music experts evaluated the creative output of the system, acknowledging an overall human-enjoyable trait of the melodies produced, which showed a solid hierarchical structure and a strong musical directionality. The techniques proposed not only have high relevance for the musical domain, but also suggest unexplored ways of using planning for dealing with non-deterministic creative domains

Goal-oriented hierarchical task networks and its application on interactive narrative planning

Two of the most commonly used AI architectures in digital games are Behavior Tree (BT) and Goal-Oriented Action Planning (GOAP). The BT architecture is script based, highly controllable but barely expandable. On the other hand the GOAP architecture is planner based, barely controllable but highly expandable. This thesis proposes a hybrid AI architecture called Goal-Oriented Hierarchical Task Network (GHTN); combining planner based approach of GOAP with script based approach of BT. GHTN modifies the Hierarchical Task Network (HTN) architecture by replacing its iterative planner with a goal oriented planner, while maintaining the BT-like scripting capabilities of HTN. GHTN's iterative-planner hybrid architecture is suitable to be used for Interactive Narrative Planning. Using GHTN with a previously crafted domain, it is possible to obtain a non-repetitive and continuous narrative flow which can also be directed by external goals. The user is presented with choices that are intelligently chosen to push the narrative towards the goal; then, depending on the answers new choices are generated. The initial state of the world and the goals are specified by a Scenarist who has the knowledge of the domain. The proposed architecture is tested on Interactive Narrative Planning task with an example domain set in the Lala Land universe, and the architecture is tested with several initial world states and goals