First Order Decision Diagrams for Relational MDPs
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1095-1099.
Abstract
Dynamic programming algorithms provide a basic tool identifying optimal solutions in Markov Decision Processes (MDP). The paper develops a representation for decision diagrams suitable for describing value functions, transition probabilities, and domain dynamics of First Order or Relational MDPs (FOMDP). By developing appropriate operations for such diagrams the paper shows how value iteration can be performed compactly for such problems. This improves on previous approaches since the representation combines compact form with efficient operations. The work also raises interesting issues on suitability of different representations to different FOMDPs tasks.
@inproceedings{ijcai2007p177,
title = {First Order Decision Diagrams for Relational MDPs},
author = {Wang, Chenggang and Joshi, Saket and Khardon, Roni},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1095--1099},
year = {2007},
url = {https://ijcai.org/proceedings/2007/177},
}
TY - CPAPER TI - First Order Decision Diagrams for Relational MDPs AU - Wang, Chenggang AU - Joshi, Saket AU - Khardon, Roni T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1095 EP - 1099 UR - https://ijcai.org/proceedings/2007/177 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
