An Operational Semantics for Knowledge Bases
Abstract
The standard approach in AI to knowledge representation isto represent an agent's knowledge symbolically as a collection of formulas, which we can view as a knowledge base. An agent is then said to know a fact if it is provable from the formulas in his knowledge base. Halpern and Vardi advocateda model-theoretic approach to knowledge representation. In this approach, the key step is representing the agent's knowl-edge using an appropriate semantic model. Here, we model knowledge bases operationally as multi-agent systems. Our results show that this approach offers significant advantages.
Cite
Text
Fagin et al. "An Operational Semantics for Knowledge Bases." AAAI Conference on Artificial Intelligence, 1994.Markdown
[Fagin et al. "An Operational Semantics for Knowledge Bases." AAAI Conference on Artificial Intelligence, 1994.](https://mlanthology.org/aaai/1994/fagin1994aaai-operational/)BibTeX
@inproceedings{fagin1994aaai-operational,
title = {{An Operational Semantics for Knowledge Bases}},
author = {Fagin, Ronald and Halpern, Joseph Y. and Moses, Yoram and Vardi, Moshe Y.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1994},
pages = {1142-1147},
url = {https://mlanthology.org/aaai/1994/fagin1994aaai-operational/}
}