(Enter summary)
Abstract: The standard approach in AI to knowledge representation is
to 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 advocated
a model-theoretic approach to knowledge representation. In
this approach, the key step is representing the agent's knowledge
using an appropriate semantic model. Here, we model
knowledge bases... (Update)
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BibTeX entry: (Update)
R. Fagin, J. Y. Halpern, Y. Moses, and M. Y. Vardi. An operational semantics for knowledge bases. In Proc. National Conference on Artificial Intelligence (AAAI '94), pages 1142--1147, 1994. http://citeseer.ist.psu.edu/fagin94operational.html More
@inproceedings{ fagin94operational,
author = "Ronald Fagin and Joseph Y. Halpern and Yoram Moses and Moshe Y. Vardi",
title = "An Operational Semantics for Knowledge Bases",
booktitle = "National Conference on Artificial Intelligence",
pages = "1142-1147",
year = "1994",
url = "citeseer.ist.psu.edu/fagin94operational.html" }
Citations (may not include all citations):
8
San Francisco (context) - pp - 1993
Documents on the same site (http://www.cs.rice.edu/~vardi/papers/index.html): More
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