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The Inductive Constraint Programming Loop.

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Title: The Inductive Constraint Programming Loop.
Authors: Bessiere, Christian1, De Raedt, Luc2, Guns, Tias2, Kotthoff, Lars3, Nanni, Mirco4, Nijssen, Siegfried2, OSullivan, Barry5, Paparrizou, Anastasia6, Pedreschi, Dino7, Simonis, Helmut5
Source: IEEE Intelligent Systems. Sep/Oct2017, Vol. 32 Issue 5, p44-52. 9p.
Subjects: Constraint programming, Computer programming, Machine learning, Data mining, Artificial intelligence
Abstract: Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn’t exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Intelligent Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: The Inductive Constraint Programming Loop.
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Intelligent+Systems%22">IEEE Intelligent Systems</searchLink>. Sep/Oct2017, Vol. 32 Issue 5, p44-52. 9p.
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  Data: Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn’t exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive constraint programming loop. In this approach, data is gathered and analyzed systematically to dynamically revise and adapt constraints and optimization criteria. Inductive constraint programming aims to bridge the gap between the areas of data mining and machine learning on one hand and constraint programming on the other. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Intelligent Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=125813139
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