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  1. Low-level game environments and other simulations present a difficulty of scale for an expensive AI technique like narrative planning, which is normally constrained to environments with small state spaces. Due to this limitation, the intentional and cooperative behavior of agents guided by this technology cannot be deployed for different systems without significant additional authoring effort. I propose a process for automatically creating models for larger-scale domains such that a narrative planner can be employed in these settings. By generating an abstract domain of an environment while retaining the information needed to produce behavior appropriate to the abstract actions, agents are able to reason in a lower-complexity space and act in the higher-complexity one. This abstraction is accomplished by the development of extended-duration actions and the identification of their preconditions and effects. Together these components may be combined to form a narrative planning domain, and plans from this domain can be executed within the low-level environment. 
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  2. 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. 
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  3. McCoy, Josh ; Treanor, Mike ; Samuel, Ben (Ed.)
    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. 
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  4. 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. 
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