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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. 

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.