
 첨부파일 : MEBNAlanguageforfirstorderBayesianknowledgebases(1).pdf (크기: 1598580 bytes)
Abstract
Although classical firstorder logic is the de facto standard logical foundation for artificial intelligence, the lack of a builtin,
semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems.
Probability is the bestunderstood and most widely applied formalism for computational scientific reasoning under uncertainty.
Increasingly expressive languages are emerging for which the fundamental logical basis is probability. This paper presents Multi
Entity Bayesian Networks (MEBN), a firstorder language for specifying probabilistic knowledge bases as parameterized fragments
of Bayesian networks. MEBN fragments (MFrags) can be instantiated and combined to form arbitrarily complex graphical probability
models. An MFrag represents probabilistic relationships among a conceptually meaningful group of uncertain hypotheses.
Thus, MEBN facilitates representation of knowledge at a natural level of granularity. The semantics of MEBN assigns a probability
distribution over interpretations of an associated classical firstorder theory on a finite or countably infinite domain. Bayesian
inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation
as evidence accrues. A proof is given that MEBN can represent a probability distribution on interpretations of any finitely
axiomatizable firstorder theory.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Bayesian network; Graphical probability models; Knowledge representation; Multientity Bayesian network; Probabilistic logic;
Uncertainty in artificial intelligence

