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The Inductive Constraint Programming Loop

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Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou
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Dino Pedreschi, Helmut Simonis

Artificial Intelligence · Submitted 12 Oct 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.

how it is stated · asserted 1.00 read the record →

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arXiv ID1510.03317 (version 1)
Pith IDS42YGOYE, Pith's permanent record of this paper and its review
CitationAnastasia Paparrizou, Barry O'Sullivan, Christian Bessiere, Dino Pedreschi, Helmut Simonis, Lars Kotthoff, Luc De Raedt, Mirco Nanni, Siegfried Nijssen, Tias Guns. "The Inductive Constraint Programming Loop." arXiv:1510.03317 (2015). https://pith.science/paper/S42YGOYE
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,762 input tokens.

What the abstract itself states, as probabilities: claims first 0.11 · states limitation 0.07 · human participants 0.05 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.02 · compares with prior results 0.28 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. background 1.00 0.04 0.02 asserted 1.00
s1 At the same time, one continuously gathers vast amounts of data about these problems. background 0.99 0.09 0.02 asserted 1.00
s2 Current constraint programming software does not exploit such data to update schedules, resources and plans. gap 0.99 0.07 0.04 asserted 1.00
s3 We propose a new framework, that we call the Inductive Constraint Programming loop. contribution 1.00 0.83 0.19 asserted 0.98
s4 In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. method 0.93 0.33 0.26 asserted 0.98
s5 Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. contribution 0.58 0.31 0.22 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.

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