Knowledge Engineering under Time and Uncertainty
From Distributed Information and Intelligence Analysis Group
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- | [[Image:ElectionBKBs.png|thumb|250 px|A Bayesian Knowledge Fragment]] | + | [[Image:ElectionBKBs.png|thumb|250 px|A Bayesian Knowledge Fragment based on a news article from the 2008 South Carolina Democratic Primary]] |
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 22:48, 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.