Relational Knowledge with Predictive State Representations
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2035-2039.
Abstract
Most work on Predictive Representations of State (PSRs) has focused on learning and planning in unstructured domains (for example, those represented by flat POMDPs). This paper extends PSRs to represent relational knowledge about domains, so that they can use policies that generalize across different tasks, capture knowledge that ignores irrelevant attributes of objects, and represent policies in a way that is independent of the size of the state space. Using a blocks world domain, we show how generalized predictions about the future can compactly capture relations between objects, which in turn can be used to naturally specify relational-style options and policies. Because our representation is expressed solely in terms of actions and observations, it has extensive semantics which are statistics about observable quantities.
@inproceedings{ijcai2007p328,
title = {Relational Knowledge with Predictive State Representations},
author = {Wingate, David and Soni, Vishal and Wolfe, Britton and Singh, Satinder},
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 = {2035--2039},
year = {2007},
url = {https://ijcai.org/proceedings/2007/328},
}
TY - CPAPER TI - Relational Knowledge with Predictive State Representations AU - Wingate, David AU - Soni, Vishal AU - Wolfe, Britton AU - Singh, Satinder T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2035 EP - 2039 UR - https://ijcai.org/proceedings/2007/328 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
