An Intent-Driven Planner for Multi-Agent Story Generation

The ability to generate narrative is of importance to computer systems that wish to use story effectively for entertainment, training, or education. We identify two properties of story -- plot coherence and character believability -- which play a role in the success of a story. Plot coherence is the perception by audience members that character actions have relevance to the outcome of the story. Character believability is the perception that character actions are motivated by agents internal beliefs and desires. Unlike conventional planning in which plan goals represent an agents intended world state, multiagent story planning involves goals that represent the outcome of a story. In order for the plans actions to appear believable, multi-agent story planners must determine not only how agents actions achieve a storys goal state, but must also ensure that each agent appears to be acting intentionally. We present a narrative generation planning system for multi-agent stories that is capable of generating narratives with both strong plot coherence and strong character believability. The planning algorithm uses causal reasoning and a simulated intention recognition process to drive plan creation.