Knowledge Engineering under Time and Uncertainty
From Distributed Information and Intelligence Analysis Group
(Difference between revisions)
(Created page with 'We work continually on developing new knowledge engineering models, frameworks, and tools. We are especially interested in reasoning under uncertainty. This can be uncertainty ...') |
|||
| Line 1: | Line 1: | ||
| + | [[Image:ElectionBKBs.png|thumb|250 px|A Bayesian Knowledge Fragment]] | ||
We work continually on developing new knowledge engineering models, frameworks, and tools. We are especially interested in reasoning under uncertainty. This can be uncertainty about the truth of a proposition or when an event may occur. The primary framework we employ is that of Bayesian Knowledge Bases (BKBs). A modeling framework that subsumes Bayesian Networks, BKBs have been extended to support temporal knowledge, and extensive work has been done on verification and validation techniques for them. | We work continually on developing new knowledge engineering models, frameworks, and tools. We are especially interested in reasoning under uncertainty. This can be uncertainty about the truth of a proposition or when an event may occur. The primary framework we employ is that of Bayesian Knowledge Bases (BKBs). A modeling framework that subsumes Bayesian Networks, BKBs have been extended to support temporal knowledge, and extensive work has been done on verification and validation techniques for them. | ||
Revision as of 22:47, 25 June 2009
We work continually on developing new knowledge engineering models, frameworks, and tools. We are especially interested in reasoning under uncertainty. This can be uncertainty about the truth of a proposition or when an event may occur. The primary framework we employ is that of Bayesian Knowledge Bases (BKBs). A modeling framework that subsumes Bayesian Networks, BKBs have been extended to support temporal knowledge, and extensive work has been done on verification and validation techniques for them.
