Constraint Programming and Machine Learning for Interactive Soccer Analysis

2016 ◽  
pp. 240-246 ◽  
Author(s):  
Robinson Duque ◽  
Juan Francisco Díaz ◽  
Alejandro Arbelaez
Keyword(s):  
Machine Learning ◽  
Constraint Programming

scholarly journals Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

2016 ◽  
Vol 12 (4) ◽  
pp. e1004838 ◽  
Author(s):  
Stephen Gang Wu ◽  
Yuxuan Wang ◽  
Wu Jiang ◽  
Tolutola Oyetunde ◽  
Ruilian Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constraint Programming ◽  
Rapid Prediction

scholarly journals Preferences in Constraint Satisfaction and Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 58 ◽  
Author(s):  
Francesca Rossi ◽  
Kristen Brent Venable ◽  
Toby Walsh
Keyword(s):  
Machine Learning ◽  
Constraint Satisfaction ◽  
Constraint Programming ◽  
Preference Elicitation ◽  
Soft Constraints ◽  
Explanation Generation ◽  
Temporal Preferences

We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.

scholarly journals Machine Learning and Constraint Programming for Relational-To-Ontology Schema Mapping

2018 ◽  
Author(s):  
Diego De Uña ◽  
Nataliia Rümmele ◽  
Graeme Gange ◽  
Peter Schachte ◽  
Peter J. Stuckey
Keyword(s):  
Machine Learning ◽  
Constraint Programming ◽  
Steiner Tree ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Schema Mapping ◽  
Heterogeneous Data Sources ◽  
Side Constraints ◽  
Flexible Framework

The problem of integrating heterogeneous data sources into an ontology is highly relevant in the database field. Several techniques exist to approach the problem, but side constraints on the data cannot be easily implemented and thus the results may be inconsistent. In this paper we improve previous work by Taheriyan et al. [2016a] using Machine Learning (ML) to take into account inconsistencies in the data (unmatchable attributes) and encode the problem as a variation of the Steiner Tree, for which we use work by De Uña et al. [2016] in Constraint Programming (CP). Combining ML and CP achieves state-of-the-art precision, recall and speed, and provides a more flexible framework for variations of the problem.

scholarly journals Learning Sensitivity of RCPSP by Analyzing the Search Process

2020 ◽  
Author(s):  
Marc-André Ménard ◽  
Claude-Guy Quimper ◽  
Jonathan Gaudreault
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Constraint Programming ◽  
Machine Learning Algorithms ◽  
Search Process ◽  
Scheduling Problem ◽  
Specific Task ◽  
Objective Value ◽  
Cumulative Constraint ◽  
The Impact

Solving the problem is an important part of optimization. An equally important part is the analysis of the solution where several questions can arise. For a scheduling problem, is it possible to obtain a better solution by increasing the capacity of a resource? What happens to the objective value if we start a specific task earlier? Answering such questions is important to provide explanations and increase the acceptability of a solution. A lot of research has been done on sensitivity analysis, but few techniques can be applied to constraint programming. We present a new method for sensitivity analysis applied to constraint programming. It collects information, during the search, about the propagation of the CUMULATIVE constraint, the filtering of the variables, and the solution returned by the solver. Using machine learning algorithms, we predict if increasing/decreasing the capacity of the cumulative resource allows a better solution. We also predict the impact on the objective value of forcing a task to finish earlier. We experimentally validate our method with the RCPSP problem.

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