[HTML][HTML] Optimization with constraint learning: A framework and survey
Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
The ASSISTANT project: AI for high level decisions in manufacturing
This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and
robuSt deciSIon SupporT systems for agile mANufacTuring environments) project …
robuSt deciSIon SupporT systems for agile mANufacTuring environments) project …
Guided bottom-up interactive constraint acquisition
Constraint Acquisition (CA) systems can be used to assist in the modeling of constraint
satisfaction problems. In (inter) active CA, the system is given a set of candidate constraints …
satisfaction problems. In (inter) active CA, the system is given a set of candidate constraints …
Learning constraints through partial queries
Learning constraint networks is known to require a number of membership queries
exponential in the number of variables. In this paper, we learn constraint networks by asking …
exponential in the number of variables. In this paper, we learn constraint networks by asking …
[HTML][HTML] A statistical approach to learning constraints
S Prestwich, N Wilson - International Journal of Approximate Reasoning, 2024 - Elsevier
A constraint-based model represents knowledge about a domain by a set of constraints,
which must be satisfied by solutions in that domain. These models may be used for …
which must be satisfied by solutions in that domain. These models may be used for …
Learning constraint networks over unknown constraint languages
C Bessiere, C Carbonnel… - IJCAI 2023-32nd …, 2023 - hal.umontpellier.fr
Constraint acquisition is the task of learning a constraint network from examples of solutions
and non-solutions. Existing constraint acquisition systems typically require advance …
and non-solutions. Existing constraint acquisition systems typically require advance …
Automating personnel rostering by learning constraints using tensors
Many problems in operations research require that constraints be specified in the model.
Determining right constraints is a hard and laborsome task. We propose an approach to …
Determining right constraints is a hard and laborsome task. We propose an approach to …
Acquiring maps of interrelated conjectures on sharp bounds
N Beldiceanu, J Cheukam-Ngouonou… - CP 2022-28th …, 2022 - hal.science
To automate the discovery of conjectures on combinatorial objects, we introduce the concept
of a map of sharp bounds on characteristics of combinatorial objects, that provides a set of …
of a map of sharp bounds on characteristics of combinatorial objects, that provides a set of …
An implicit learning approach for solving the nurse scheduling problem
Abstract The Nurse Scheduling Problem (NSP) is one of the challenging combinatorial
optimization problems encountered in the healthcare sector. Solving the NSP consists in …
optimization problems encountered in the healthcare sector. Solving the NSP consists in …
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling
Solving combinatorial optimization problems involve satisfying a set of hard constraints
while optimizing some objectives. In this context, exact or approximate methods can be …
while optimizing some objectives. In this context, exact or approximate methods can be …