دانلود مقاله ISI انگلیسی شماره 111678
ترجمه فارسی عنوان مقاله

برنامه ریزی خطی رابطه ای

عنوان انگلیسی
Relational linear programming
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
111678 2017 67 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Artificial Intelligence, Volume 244, March 2017, Pages 188-216

ترجمه کلمات کلیدی
فراگیری ماشین، بهینه سازی، منطق ارتباطی، یادگیری آماری آماری، برنامه ریزی خطی، تقارن، (تقسیم) رنگ پالایش، استنتاج احتمال احتمالی، برنامه ریزی خطی بلند پارتیشن های عادلانه پارتیشن مدار،
کلمات کلیدی انگلیسی
Machine learning; Optimization; Relational logic; Statistical relational learning; Linear programming; Symmetry; (Fractional) automorphism; Color-refinement; Lifted probabilistic inference; Lifted linear programming; Equitable partitions; Orbit partitions;
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چکیده انگلیسی

We propose relational linear programming, a simple framework for combining linear programs (LPs) and logic programs. A relational linear program (RLP) is a declarative LP template defining the objective and the constraints through the logical concepts of objects, relations, and quantified variables. This allows one to express the LP objective and constraints relationally for a varying number of individuals and relations among them without enumerating them. Together with a logical knowledge base, effectively a logic program consisting of logical facts and rules, it induces a ground LP. This ground LP is solved using lifted linear programming. That is, symmetries within the ground LP are employed to reduce its dimensionality, if possible, and the reduced program is solved using any off-the-shelf LP solver. In contrast to mainstream LP template languages such as AMPL, which features a mixture of declarative and imperative programming styles, RLP's relational nature allows a more intuitive representation of optimization problems, in particular over relational domains. We illustrate this empirically by experiments on approximate inference in Markov logic networks using LP relaxations, on solving Markov decision processes, and on collective inference using LP support vector machines.