Learned Models for Continuous Planning (1999)
| Venue: | In Proceedings of Uncertainty 99: The Seventh International Workshop on Artificial Intelligence and Statistics |
| Citations: | 14 - 4 self |
BibTeX
@INPROCEEDINGS{Schmill99learnedmodels,
author = {Matthew D. Schmill and Tim Oates and Paul R. Cohen},
title = {Learned Models for Continuous Planning},
booktitle = {In Proceedings of Uncertainty 99: The Seventh International Workshop on Artificial Intelligence and Statistics},
year = {1999},
pages = {278--282},
publisher = {Morgan Kaufmann Publishers, Inc}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
We are interested in the nature of activity -- structured behavior of nontrivial duration -- in intelligent agents. We believe that the development of activity is a continual process in which simpler activities are composed, via planning, to form more sophisticated ones in a hierarchical fashion. The success or failure of a planner depends on its models of the environment, and its ability to implement its plans in the world. We describe an approach to generating dynamical models of activity from real-world experiences and explain how they can be applied towards planning in a continuous state space. 1 Introduction We are interested in the problem of how activity emerges in an intelligent agent. We believe that activity plays a critical role in the development of many high-level cognitive structures: classes, concepts, and language, to name a few. Thus, it is our goal to derive and implement a theory of the development of activity in intelligent agents and implement it using the Pioneer...
Citations
No citations identified.
