Plan Recognition in Stories and in Life
This work addresses a specific issue in computational linguistics and AI for understanding narrative comprehension, but it is incremental as it builds on existing theories of plan recognition.
The paper tackles the problem of how plan recognition differs between stories and real life, proposing a Bayesian network formalization of a first-order theory of plans and identifying a parameter that governs this difference, with results showing how speaker topic selection influences this parameter to align with story relevance.
Plan recognition does not work the same way in stories and in "real life" (people tend to jump to conclusions more in stories). We present a theory of this, for the particular case of how objects in stories (or in life) influence plan recognition decisions. We provide a Bayesian network formalization of a simple first-order theory of plans, and show how a particular network parameter seems to govern the difference between "life-like" and "story-like" response. We then show why this parameter would be influenced (in the desired way) by a model of speaker (or author) topic selection which assumes that facts in stories are typically "relevant".