Relational linear programming
| Autor: | Kristian Kersting, Martin Mladenov, Pavel Tokmakov |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Linguistics and Language
Theoretical computer science Linear programming business.industry Statistical relational learning Inference AMPL 0102 computer and information sciences 02 engineering and technology 01 natural sciences Language and Linguistics Approximate inference Relational calculus Imperative programming 010201 computation theory & mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Markov decision process Artificial intelligence business computer Mathematics computer.programming_language |
| Zdroj: | Artificial Intelligence. 244:188-216 |
| ISSN: | 0004-3702 |
| DOI: | 10.1016/j.artint.2015.06.009 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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