Bi-objective Discrete Graphical Model Optimization

Samuel Buchet, David Allouche, Simon de Givry, Thomas Schiex

Abstract: Discrete Graphical Models (GMs) are widely used in Artificial Intelligence to describe complex systems through a joint function of interest. Probabilistic GMs such as Markov Random Fields (MRFs) define a joint non-normalized probability distribution while deterministic GMs such as Cost Function Networks (CFNs) define a joint cost function. A typical query on GMs consists in finding the joint state that optimizes this joint function, a problem denoted as the Maximum a Posteriori or Weighted Constraint Satisfaction Problem respectively.