Lazy Conflict Detection with Genetic Algorithms

The customization of complex products and services requires configurators with often large and complex knowledge bases. In the case that configuration-related user requirements are inconsistent with the knowledge base, immediate feedback is desired. However, due to the domain’s complexity, efficient...

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Published inRecent Trends and Future Technology in Applied Intelligence Vol. 10868; pp. 175 - 186
Main Authors Uran, Christoph, Felfernig, Alexander
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN331992057X
9783319920573
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-92058-0_17

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Abstract The customization of complex products and services requires configurators with often large and complex knowledge bases. In the case that configuration-related user requirements are inconsistent with the knowledge base, immediate feedback is desired. However, due to the domain’s complexity, efficient feedback generation is often not possible. In this paper we show how to use genetic algorithms to pre-generate minimal conflict sets. Their integration into the configurator allows response times required for interactive settings. Our evaluations, based on knowledge bases from the air pollution monitoring domain, show significant performance improvements.
AbstractList The customization of complex products and services requires configurators with often large and complex knowledge bases. In the case that configuration-related user requirements are inconsistent with the knowledge base, immediate feedback is desired. However, due to the domain’s complexity, efficient feedback generation is often not possible. In this paper we show how to use genetic algorithms to pre-generate minimal conflict sets. Their integration into the configurator allows response times required for interactive settings. Our evaluations, based on knowledge bases from the air pollution monitoring domain, show significant performance improvements.
Author Felfernig, Alexander
Uran, Christoph
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Mouhoub, Malek
Sadaoui, Samira
Ait Mohamed, Otmane
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Notes The work presented in this paper was partially funded by the European Commission via the Horizon 2020 project AGILE (https://agile-iot.eu/).
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Snippet The customization of complex products and services requires configurators with often large and complex knowledge bases. In the case that configuration-related...
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Title Lazy Conflict Detection with Genetic Algorithms
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