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First-Order Mixed Integer Linear Programming (2009)

By: Geoff Gordon, Sue Ann Hong, and Miroslav Dudik

Abstract: Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inference in FOL for corresponding problems.

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Geoff Gordon, Sue Ann Hong, and Miroslav Dudik (2009). "First-Order Mixed Integer Linear Programming." Conference on Uncertainty in Artificial Intelligence (UAI). pdf            
BibTeX citation

title = {First-Order Mixed Integer Linear Programming},
author = {Geoff Gordon and Sue Ann Hong and Miroslav Dudik},
booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
month = {July},
year = {2009},
address = {Montreal, Canada},
wwwfilebase = {uai2009-gordon-hong-dudik},
wwwtopic = {Representation},

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