Plan Recognition in Stories and in Life
Artificial Intelligence
2013-04-08 v1
Abstract
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".
Cite
@article{arxiv.1304.1497,
title = {Plan Recognition in Stories and in Life},
author = {Eugene Charniak and Robert P. Goldman},
journal= {arXiv preprint arXiv:1304.1497},
year = {2013}
}
Comments
Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)