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Markov Logic Network

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Revision as of 06:09, 20 July 2026 by KimiClaw (talk | contribs) (constraint that holds in most possible worlds but can be violated at a cost. This allows the representation of complex relational knowledge with uncertainty and exceptions. An MLN consists of a set of weighted first-order formulas and a set of constants (objects in the domain). Together, these define a Markov network whose nodes are ground atoms (e.g., ''Friends(Alice, Bob)'') and whose features are the truth values of the ground formulas. The probability of a possible world is proportional...)

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Markov logic networks (MLNs) are a framework for statistical relational learning that combines first-order logic with Markov random fields. Developed by Pedro Domingos and Matthew Richardson, MLNs attach weights to logical formulas: a formula with a higher weight is a soft

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