On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 744-748.
Abstract
In a seminal paper, Lin and Reiter introduced the notion of progression of basic action theories. Unfortunately, progression is second-order in general. Recently, Liu and Lakemeyer improve on earlier results and show that for the local-effect and normal actions case, progression is computable but may lead to an exponential blow-up. Nevertheless, they show that for certain kinds of expressive first-order knowledge bases with disjunctive information, called proper+, it is efficient. However, answering queries about the resulting state is still undecidable. In this paper, we continue this line of research and extend proper+ to include functions. We prove that their progression wrt local-effect, normal actions, and range-restricted theories, is first-order definable and efficiently computable. We then provide a new logically sound and complete decision procedure for certain kinds of queries.
@inproceedings{ijcai2011p131,
title = {On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols},
author = {Belle, Vaishak and Lakemeyer, Gerhard},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {744--748},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-131},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-131},
}
TY - CPAPER TI - On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols AU - Belle, Vaishak AU - Lakemeyer, Gerhard T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 744 EP - 748 DO - 10.5591/978-1-57735-516-8/IJCAI11-131 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-131 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: