000 02561nam a22005295i 4500
001 978-3-319-72691-5
003 DE-He213
005 20220801221230.0
007 cr nn 008mamaa
008 171230s2018 sz | s |||| 0|eng d
020 _a9783319726915
_9978-3-319-72691-5
024 7 _a10.1007/978-3-319-72691-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aAkama, Seiki.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954839
245 1 0 _aReasoning with Rough Sets
_h[electronic resource] :
_bLogical Approaches to Granularity-Based Framework /
_cby Seiki Akama, Tetsuya Murai, Yasuo Kudo.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aX, 201 p. 12 illus., 7 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v142
520 _aThis book explores reasoning with rough sets by developing a granularity-based framework. It begins with a brief description of rough set theory. Next, we examine some relations between rough set theory and non-classical logics including modal logic. We also develop a granularity-based framework for reasoning in which various types of reasoning can be formalized. This book will be of interest to researchers working on the areas in Artificial Intelligence, database and logic.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aMurai, Tetsuya.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954840
700 1 _aKudo, Yasuo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954841
710 2 _aSpringerLink (Online service)
_954842
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319726908
776 0 8 _iPrinted edition:
_z9783319726922
776 0 8 _iPrinted edition:
_z9783319891958
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v142
_954843
856 4 0 _uhttps://doi.org/10.1007/978-3-319-72691-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79439
_d79439