A Model of Decidable Introspective Reasoning with Quantifying-In
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 492-496.
Abstract
Since knowledge is usually incomplete, agents need to introspect on what they know and do not know . The best known models of introspective reasoning suffer from intractability or even undecidability if the underlying language is first-order . To better suit the fact that agents have limited resources, we recently proposed a model of decidable introspective reasoning in first-order knowledge bases (KBs). However, this model is deficient in that it does not allow for quantifying-in, which is needed to distinguish between knowing that and knowing who. In this paper, we extend our earlier work by adding quantifying-in and equality to a model of limited belief that integrates ideas from possible-world semantics and relevance logic.
@inproceedings{ijcai1991p74,
title = {A Model of Decidable Introspective Reasoning with Quantifying-In},
author = {Lakemeyer, Gerhard},
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 = {492--496},
year = {1991},
month = {8},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1991/74},
}
TY - CPAPER TI - A Model of Decidable Introspective Reasoning with Quantifying-In AU - Lakemeyer, Gerhard T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 492 EP - 496 UR - https://ijcai.org/proceedings/1991/74 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
