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dc.contributor.authorDelmotte, Florent
dc.contributor.authorEgerstedt, Magnus B.
dc.contributor.authorAustin, Adam
dc.date.accessioned2011-04-08T16:55:14Z
dc.date.available2011-04-08T16:55:14Z
dc.date.issued2004
dc.identifier.citationF. Delmotte, M. Egerstedt, and A. Austin. Data-Driven Generation of Low-Complexity Control Programs. International Journal of Hybrid Systems, Vol. 4, No. 1&2, pp. 53-72, March & June 2004.en_US
dc.identifier.issn1534-0422
dc.identifier.urihttp://hdl.handle.net/1853/38458
dc.description.abstractIn this paper we study the problem of generating control programs, i.e. strings of symbolic descriptions of control-interrupt pairs (or modes) from input-output data. In particular, we take the point of view that such control programs have an information theoretic content and thus that they can be more or less effectively coded. As a result, we focus our attention on the problem of producing low-complexity programs by recovering the shortest mode strings as well as the strings that contain the smallest number of distinct modes. An example is provided where the data is obtained by tracking ten roaming ants in a tank.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectControl programsen_US
dc.subjectMotion description languagesen_US
dc.subjectMulti-modal control lawsen_US
dc.titleData-Driven Generation of Low-Complexity Control Programsen_US
dc.typeArticleen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.publisher.originalNonpareil Publishers


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