ModelSeeker: Extracting Global Constraint Models from Positive Examples

نویسندگان

  • Nicolas Beldiceanu
  • Helmut Simonis
چکیده

Sequence Generator Projection Constraint Conjunction 1 scheme(612,34,18,34,1) id alldifferent*18 2 scheme(612,34,18,2,2) id alldifferent*153 3 scheme(612,34,18,1,18) id alldifferent*34 4 scheme(612,34,18,1,18) absolute value symmetric alldifferent([1..18])*34 5 scheme(612,34,18,17,1) absolute value alldifferent*36 6 repart(612,34,18,34,9) id sum ctr(0)*306 7 repart(612,34,18,34,9) id twin*1 8 repart(612,34,18,34,9) id elements([i,-i ])*1 9 first(9,[1,3,5,7,9,11,13,15,17]) id strictly increasing*1 10 vector(612) id global cardinality([-18.. -1-17,0-0,1..18-17])*1 11 repart(612,34,18,34,9) id sum powers5 ctr(0)*306 12 repart(612,34,18,34,9) id sum cubes ctr(0)*306 13 repart(612,34,18,34,3) sign global cardinality([-1-3,0-0,1-3])*102 14 scheme(612,34,18,34,1) sign global cardinality([-1-17,0-0,1-17])*18 15 repart(612,34,18,17,9) sign global cardinality([-1-2,0-0,1-2])*153 16 repart(612,34,18,2,9) sign global cardinality([-1-17,0-0,1-17])*18 17 scheme(612,34,18,1,18) sign global cardinality([-1-9,0-0,1-9])*34 18 repart(612,34,18,34,9) sign sum ctr(0)*306 19 repart(612,34,18,34,9) sign twin*1 20 repart(612,34,18,34,9) absolute value twin*1 21 repart(612,34,18,34,9) sign elements([i,-i ])*1 22 scheme(612,34,18,34,1) sign among seq(3,[-1])*18 23 repart(612,34,18,34,9) absolute value elements([i,i ])*1 24 first(9,[1,3,5,7,9,11,13,15,17]) absolute value strictly increasing*1 25 first(6,[1,4,7,10,13,16]) absolute value strictly increasing*1 26 scheme(612,34,18,34,1) absolute value nvalue(17)*18 Selected Example Results

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using the Global Constraint Seeker for Learning Structured Constraint Models: A First Attempt

Considering problems that have a strong internal structure, this paper shows how to generate constraint models from a set of positive, flat samples (i.e., solutions) without knowing a priori neither the constraint candidates, nor the way variables are shared within constraints. We describe two key contributions to building such a model generator: (1) First, learning is modeled as a bi-criteria ...

متن کامل

A Constraint Seeker: Finding and Ranking Global Constraints from Examples

In this paper we describe a Constraint Seeker application which provides a web interface to search for global constraints in the global constraint catalog, given positive and negative ground examples. Based on the given instances the tool returns a ranked list of matching constraints, the rank indicating whether the constraint is likely to be the intended constraint of the user. We give some ex...

متن کامل

Learning Structured Constraint Models: a First Attempt

In this paper we give an overview of an early prototype which learns structured constraint models from flat, positive examples of solutions. It is based on previous work on a Constraint Seeker, which finds constraints in the global constraint catalog satisfying positive and negative examples. In the current tool we extend this system to find structured conjunctions of constraints on regular sub...

متن کامل

Volumetric medical images segmentation using shape constrained deformable models

In this paper we address the problem of extracting geometric models from low contrast volumetric images, given a template or reference shape of that model. We proceed by deforming a reference model in a volumetric image. This reference deformable model is represented as a simplex mesh submitted to regularizing shape constraint. Furthermore, we introduce an original approach that combines the de...

متن کامل

Learning Parameters for the Sequence Constraint from Solutions

This paper studies the problem of learning parameters for global constraints such as Sequence from a small set of positive examples. The proposed technique computes the probability of observing a given constraint in a random solution. This probability is used to select the more likely constraint in a list of candidates. The learning method can be applied to both soft and hard constraints.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012