Multi-Agent Narrative Experience Management as Story Graph Pruning

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By never pruning player actions, the experience manager can accommodate any player choice. This experience management technique was first implemented on a training simulation, where participants’ performance improved over repeated sessions. This technique was also employed on an adventure game where players generally found the NPCs’ behaviors to be more believable than the control