Explanations for over-constrained problems using QuickXPlain with speculative executions
Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an in...
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| Veröffentlicht in: | Journal of intelligent information systems 2021-12, Vol.57 (3), p.491-508 |
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| creator | Vidal, Cristian Felfernig, Alexander Galindo, José Atas, Müslüm Benavides, David |
| description | Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s
QuickXPlain
is a divide-and-conquer based algorithm for the detection of
preferred minimal conflicts
. In this article, we present a novel approach to the detection of such conflicts which is based on
speculative programming
. We introduce a parallelization of
QuickXPlain
and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized
QuickXPlain
version. |
| doi_str_mv | 10.1007/s10844-021-00675-4 |
| format | Article |
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QuickXPlain
is a divide-and-conquer based algorithm for the detection of
preferred minimal conflicts
. In this article, we present a novel approach to the detection of such conflicts which is based on
speculative programming
. We introduce a parallelization of
QuickXPlain
and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized
QuickXPlain
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QuickXPlain
is a divide-and-conquer based algorithm for the detection of
preferred minimal conflicts
. In this article, we present a novel approach to the detection of such conflicts which is based on
speculative programming
. We introduce a parallelization of
QuickXPlain
and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized
QuickXPlain
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Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker’s
QuickXPlain
is a divide-and-conquer based algorithm for the detection of
preferred minimal conflicts
. In this article, we present a novel approach to the detection of such conflicts which is based on
speculative programming
. We introduce a parallelization of
QuickXPlain
and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized
QuickXPlain
version.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10844-021-00675-4</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-1600-3447</orcidid></addata></record> |
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| source | SpringerNature Journals |
| subjects | Algorithms Artificial Intelligence Computer Science Conflict resolution Constraints Data Structures and Information Theory Decision making Information Storage and Retrieval Information systems IT in Business Knowledge Knowledge bases (artificial intelligence) Knowledge representation Natural Language Processing (NLP) Performance evaluation Software |
| title | Explanations for over-constrained problems using QuickXPlain with speculative executions |
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