New Approaches to Constraint Acquisition

Bessière, Christian; Daoudi, Abderrazak; Hébrard, Emmanuel; Katsirelos, George; Lazaar, Nadjib; Mechqrane, Younes; Narodytska, Nina; Quimper, Claude-Guy; Walsh, Toby

doi:10.1007/978-3-319-50137-6_3

Cited by 25 publications

(48 citation statements)

References 12 publications

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“…Future work includes further improving the run-time of IN-CAL, for instance by incorporating warm starts [Bertsimas and Dunn, 2017] (e.g., exploiting incremental SMT solving also in the parameter learning step), using heuristics for example selection in the iterative step (e.g., by incorporating heuristics used in active learning) and learning from partial assignments [Bessiere et al, 2013]. We also plan to investigate learning from noisy labels by either tweaking the encoding or subsampling the dataset and supporting integer variables and equalities.…”

Section: Resultsmentioning

confidence: 99%

Learning SMT(LRA) Constraints using SMT Solvers

Kolb

Teso

Passerini

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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We introduce the problem of learning SMT(LRA) constraints from data. SMT(LRA) extends propositional logic with (in)equalities between numerical variables. Many relevant formal verification problems can be cast as SMT(LRA) instances and SMT(LRA) has supported recent developments in optimization and counting for hybrid Boolean and numerical domains. We introduce SMT(LRA) learning, the task of learning SMT(LRA) formulas from examples of feasible and infeasible instances, and we contribute INCAL, an exact non-greedy algorithm for this setting. Our approach encodes the learning task itself as an SMT(LRA) satisfiability problem that can be solved directly by SMT solvers. INCAL is an incremental algorithm that achieves exact learning by looking only at a small subset of the data, leading to significant speed-ups. We empirically evaluate our approach on both synthetic instances and benchmark problems taken from the SMT-LIB benchmarks repository.

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Section: Resultsmentioning

confidence: 99%

Learning SMT(LRA) Constraints using SMT Solvers

Kolb

Teso

Passerini

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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“…Constraint learning facilitates this step by automatically extracting a model from data. Several constraint learning approaches have been designed for learning constraint programs and mathematical optimization models, see, e.g., (Rossi and Sperduti 2004;Bessiere et al 2005Bessiere et al , 2016Beldiceanu and Simonis 2012). TaCLe (Kolb et al 2017) is an approach specifically tailored for the spreadsheet setting.…”

Section: Predictive Spreadsheet Autocompletionmentioning

confidence: 99%

Predictive spreadsheet autocompletion with constraints

et al. 2019

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Spreadsheets are arguably the most accessible data-analysis tool and are used by millions of people. Despite the fact that they lie at the core of most business practices, working with spreadsheets can be error prone, usage of formulas requires training and, crucially, spreadsheet users do not have access to state-of-the-art analysis techniques offered by machine learning. To tackle these issues, we introduce the novel task of predictive spreadsheet autocompletion, where the goal is to automatically predict the missing entries in the spreadsheets. This task is highly non-trivial: cells can hold heterogeneous data types and there might be unobserved relationships between their values, such as constraints or probabilistic dependencies. Critically, the exact prediction task itself is not given. We consider a simplified, yet non-trivial, setting and propose a principled probabilistic model to solve it. Our approach combines black-box predictive models specialized for different predictive tasks (e.g., classification, regression) and constraints and formulas detected by a constraint learner, and produces a maximally likely prediction for all target cells that is consistent with the constraints. Overall, our approach brings us one step closer to allowing end users to leverage machine learning in their workflows without writing a single line of code.

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“…Many propose interactive constraint acquisition systems learning a complete CSP from examples by asking queries to the user. Among the most recent publications : Bessiere et al [10,15,14], Daoudi et al [20], and Arcangioli and Lazaar [1].…”

Section: Constraint Acquisitionmentioning

confidence: 99%

Learning the Parameters of Global Constraints Using Branch-and-Bound

Picard-Cantin

Bouchard²,

Quimper

et al. 2017

Lecture Notes in Computer Science

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Precise constraint satisfaction modeling requires specific knowledge acquired from multiple past cases. We address this issue with a general branch-and-bound algorithm that learns the parameters of a given global constraint from a small set of positive solutions. The idea is to cleverly explore the possible combinations taken by the constraint's parameters without explicitly enumerating all combinations. We apply our method to learn parameters of global constraints used in timetabling problems such as Sequence and SubsetFocus. The later constraint is our adaptation of the constraint Focus to timetabling problems.

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New Approaches to Constraint Acquisition

Cited by 25 publications

References 12 publications

Learning SMT(LRA) Constraints using SMT Solvers

Learning SMT(LRA) Constraints using SMT Solvers

Predictive spreadsheet autocompletion with constraints

Learning the Parameters of Global Constraints Using Branch-and-Bound

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