Nonmonotonic Databases and Epistemic Queries
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 381-385.
Abstract
The approach to database query evaluation developed by Levesque and Reiter treats databases as first order theories, and queries as formulas of the language which includes, in addition to the language of the database, an epistemic modal operator . In this epistemic query language, one can express questions not only about the external world described by the database, but also about the database itselfabout what the database knows. On the other hand, epistemic formulas are used in knowledge representation for the purpose of expressing defaults. Autoepistemic logic is the best known epistemic nonmonotonic formalism· the logic of grounded knowledge, proposed rec~ntly by Lin and Shoham, is another such system. This paper brings these two directions of research together. We describe a new version of the Lin/Shoham logic, similar in spirit to the Levesque/Reiter theory of epistemic queries. Using this formalism, we can give meaning to epistemic queries in the context of nonmonotonic databases, including logic programs with negation as failure.
@inproceedings{ijcai1991p57,
title = {Nonmonotonic Databases and Epistemic Queries},
author = {Lifschitz, Vladimir},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {381--385},
year = {1991},
month = {8},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1991/57},
}
TY - CPAPER TI - Nonmonotonic Databases and Epistemic Queries AU - Lifschitz, Vladimir T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 381 EP - 385 UR - https://ijcai.org/proceedings/1991/57 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: