# Markov Logic Networks

## Markov Logic Networks (Matthew Richardson & Pedro Domingos, 2006).

### Problem

Traditionally, first-order logic imposes hard constraints on the world. This poses problems in the real world: formulae that may be typically true in the real world are not always true. In most domains, it is difficult to devise non-trivial formulae that are always true. Probabilistic graphical models is a decent solution.

### What are Markov Logic Networks ?

Markov logic networks relax the hard constraints that first-order logic enforces. When a world violates one formula in a KB, it is less probable, but not impossible. The fewer formulae a world violates, the more probable it is. Each formula is associated with a weight that reflects how strong a constraint it is: the higher the weight, the greater the difference in log probability between a world that satisfies the formula, and one that does not, other things equal.

Formally,

A Markov Logic Network $$L$$ is a set of pairs $$(F_i, w_i)$$, where $$F_i$$ is a formula in first-order logic, and $$w_i$$ is a real number. Together with a finite set of constants $$C = \left\{ c_1, c-2, \dots, c_{|C|} \right\}$$, it defines a Markov Logic Network as follows:

1. $$M_{L,C}$$ contains one binary node for e ach possible grounding of each predicate appearing in $$L$$. The binary node takes on value $$1$$ if the ground atom is true, and 0 otherwise.
2. $$M_{L,C}$$ contains one feature for each possible grounding of each formula $$F_i$$ in $$L$$. The value of this feature is $$1$$ if the ground formula is true, and 0 otherwise. The weight of the feature is the $$w_i$$ associated with $$F_i$$ in $$L$$.

# Bibliography

Icon by Laymik from The Noun Project. Website built with ♥ with Org-mode, Hugo, and Netlify.