MEBN: A language for first-order Bayesian knowledge bases

By Kathryn B. Laskey. Artificial Intelligence, 2008

316 citations recorded by the source corpus.

Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the best-understood 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 first-order 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 first-order 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 first-order theory.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5a499e1d1b42093e1b8c0bc3d74200e183f32392

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In Artificial Intelligence

1. Michael Beetz
2. Moritz Tenorth
3. Malik Ghallab
4. F. Ingrand

Citations in the corpus

• KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “…of undirected (e.g. Markov logic networks (Richardson and Domingos, 2006)) or directed graphical models (e.g. multientity Bayesian networks (Laskey, 2008) or Bayesian logic networks (Jain et al., 2009)) and can therefore make use of inference methods that have been developed for these…”