Helmut Simonis

IS Journal 2017 Journal Article

The Inductive Constraint Programming Loop

• Christian Bessiere
• Luc De Raedt
• Tias Guns
• Lars Kotthoff
• Mirco Nanni
• Siegfried Nijssen
• Barry O'Sullivan
• Anastasia Paparrizou

Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn't exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other.

SoCS Conference 2013 Conference Paper

GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials

• Naina Razakarison
• Mats Carlsson
• Nicolas Beldiceanu
• Helmut Simonis

We provide a filtering algorithm achieving GAC for the conjunction of constraints atleast (b, [x(0), x(1), .. ., x(n-1)], V) and (a(0)*x(0) +. .. + a(n-1)*x(n-1)) <= c, where the atleast constraint enforcesb variables out of x(0), x(1), .. ., x(n-1) to be assigned to avalue in the set V. This work was motivated by learning simplepolynomials, i. e. finding the coefficients of polynomialsin several variables from example parameter and function values. We additionally require that coefficients be integers, andthat most coefficients be assigned to zero or integers close to0. These problems occur in the context of learning constraintmodels from sample solutions of different sizes. Experimentswith this more global filtering show an improvement by severalorders of magnitude compared to handling the constraintsin isolation or with cost gcc, while also out-performing a0/1 MIP model of the problem.

AIJ Journal 1992 Journal Article

Constraint satisfaction using constraint logic programming

• Pascal Van Hentenryck
• Helmut Simonis
• Mehmet Dincbas

Constraint logic programming (CLP) is a new class of declarative programming languages whose primitive operations are based on constraints (e. g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solving a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: test-pattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools.

ECAI Conference 1988 Conference Paper

Solving the Car-Sequencing Problem in Constraint Logic Programming

• Mehmet Dincbas
• Helmut Simonis
• Pascal Van Hentenryck