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dc.contributor.authorMartine Ceberioen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorRujira Oncharoenen_US
dc.description.abstract© 2015 IEEE. In the general case, most computational engineering problems are NP-hard. So, to make the problem feasible, it is important to restrict this problem. Ideally, we should use the most general context in which the problem is still feasible. In this paper, we prove that finding such most general context is itself an NP-hard problem. Since it is not possible to find the appropriate context by utilizing some algorithm, it is therefore necessary to be creative-i.e., To use some computational intelligence techniques. On three examples, we show how such techniques can help us come up with the appropriate context. Our analysis explains why it is beneficial to take knowledge about causality into account when processing data, why sometimes long-Term predictions are easier than short-Term ones, and why often for small deviations, a straightforward application of a seemingly optimal control only makes the situation worse.en_US
dc.subjectComputer Scienceen_US
dc.titleWhat is the right context for an engineering problem: Finding such a context is NP-harden_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015en_US of Texas at El Pasoen_US Mexico State University Las Crucesen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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