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We present an intelligent experience management architecture for a virtual reality police de-escalation training platform we are currently developing. Our aim is to direct the cast of non-player characters toward a scenario outcome appropriate to the player’s decisions, resulting in bad endings precisely when player’s mistakes enable them. We use a narrative planner to generate a story graph representing every possible narrative, and then we prune the graph to eliminate less believable non-player character actions. Unlike previous approaches based on story graph pruning, we implement an emotional planning model that lets us represent characters acting out of fear of bad outcomes as well as hope for good ones. We also incorporate experience management techniques for delaying commitment to hidden settings of the scenario and for capitalizing on player mistakes to demonstrate the negative consequences of not following best practices. 

Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems. 

Psychological research has demonstrated that as we experience a story several features affect the salience of its events in memory. These features correspond to who? where? when? how? and why? questions about those events. Computational models of salience have been used in interactive narratives to measure which events people most easily remember from the past and which they expect more readily from the future. We use three example domains to show that events in sequences that are solutions to narrative planning problems are generally more salient with each other, and events in non-solution sequences are less salient with each other. This means that measuring the salience of a sequence of actions during planning can serve as an efficient cost function to improve the speed, and perhaps also the quality, of a narrative planner. 

Intelligent interactive narrative systems coordinate a cast of non-player characters to make the overall story experience meaningful for the player. Narrative generation involves a tradeoff between plot-structure requirements and quality of character behavior, as well as computational efficiency. We study this tradeoff using the example of benchmark problems for narrative planning algorithms. A typical narrative planning problem calls for a sequence of actions that leads to an overall plot goal being met, while also requiring each action to respect constraints that create the appearance of character autonomy. We consider simplified solution definitions that enforce only plot requirements or only character requirements, and we measure how often each of these definitions leads to a solution that happens to meet both types of requirements—i.e., the density with which narrative plans occur among plot- or character-requirement-satisfying sequences. We then investigate whether solution densities can guide the selection of narrative planning algorithms. We compare the performance of two search strategies: one that satisfies plot requirements first and checks character requirements afterward, and one that continuously verifies character requirements. Our results show that comparing solution densities does not by itself predict which of these search strategies will be more efficient in terms of search nodes visited, suggesting that other important factors exist. We discuss what some of these factors could be. Our work opens further investigation into characterizing narrative planning algorithms and how they interact with specific domains. The results also highlight the diversity and difficulty of solving narrative planning problems. 

Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent’s individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system’s performance on several narrative planning benchmark problems. 

Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent’s individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system’s performance on several narrative planning benchmark problems. 

Narrative generation systems can be classified on a spectrum from strong autonomy to strong story. Systems on the strong autonomy side treat characters as fully independent agents but may struggle to meet the author’s requirements, while those on the strong story side direct character behaviors centrally but may struggle to create the illusion of character believability. In this paper, we use benchmark story generation problems as a framework to compare the spaces of stories that could be generated by prototypical strong story and strong autonomy systems. Comparing the relative solution densities of these spaces helps us quantify how common certain desirable narrative properties are. This can be informative for system designers when deciding, for instance, whether to strictly enforce all desired properties or to generate and filter from a broader class of solutions. 

A valid and believable narrative plan must often meet at least two requirements: the author’s goal must be satisfied by the end, and every action taken must make sense based on the goals and beliefs of the characters who take them. Many narrative planners are based on progression, or forward search through the space of possible states. When reasoning about goals and beliefs, progression can be wasteful, because either the planner needs to satisfy the author’s goal first and then explain actions, backtracking when an explanation cannot be found, or explain actions as they are taken, which may waste effort explaining actions that are not relevant to the author’s goal. We propose that regression, or backward search from goals, can address this problem. Regression ensures that every action sequence is intentional and only reasons about the agent beliefs needed for a plan to make sense. 

In this paper we describe a virtual reality training simulation designed to help police officers learn use of force policies. Our goal is to test a training simulation prototype by measuring improvements to presence and performance. If successful, this can lead to creating a full-scale virtual reality narrative training simulation. The simulation uses a planner-based experience manager to determine the actions of agents other than the participant. Participants’ actions were logged, physiological data was recorded, and the participants filled out questionnaires. Player knowledge attributes were authored to measure participants’ understanding of teaching materials. We demonstrate that when participants interact with the simulation using virtual reality they experience greater presence than when using traditional screen and keyboard controls. We also demonstrate that participants’ performance improves over repeated sessions. 

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.