Plan Recognition in Stories and in Life
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
Text
Charniak and Goldman. "Plan Recognition in Stories and in Life." Conference on Uncertainty in Artificial Intelligence, 1989.Markdown
[Charniak and Goldman. "Plan Recognition in Stories and in Life." Conference on Uncertainty in Artificial Intelligence, 1989.](https://mlanthology.org/uai/1989/charniak1989uai-plan/)BibTeX
@inproceedings{charniak1989uai-plan,
title = {{Plan Recognition in Stories and in Life}},
author = {Charniak, Eugene and Goldman, Robert P.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1989},
url = {https://mlanthology.org/uai/1989/charniak1989uai-plan/}
}