LEADER 01091nam0 22003013i 450 001 SUN0128717 005 20200505094215.203 010 $a8-3-319-73708-9$d0.00 017 70$2N$a978-3-319-73709-6 100 $a20200505d2018 |0engc50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $a*Anticoagulation Therapy$fJoe F. Lau, Geoffrey D. Barnes, Michael B. Streiff editors 205 $aCham : Springer, 2018 210 $axxix$d452 p. ; 24 cm 215 $aPubblicazione in formato elettronico 620 $aCH$dCham$3SUNL001889 702 1$aLau$b, Joe F.$3SUNV100628 702 1$aBarnes$b, Geoffrey D.$3SUNV100629 702 1$aStreiff$b, Michael B.$3SUNV100630 712 $aSpringer$3SUNV000178$4650 801 $aIT$bSOL$c20200921$gRICA 856 4 $uhttps://link.springer.com/book/10.1007%2F978-3-319-73709-6 912 $aSUN0128717 950 $aBIBLIOTECA CENTRO DI SERVIZIO SBA$d15CONS SBA EBOOK 6094 $e15EB 6094 20200505 996 $aAnticoagulation Therapy$91742591 997 $aUNICAMPANIA LEADER 02966nam 22005655 450 001 996466161203316 005 20200702084941.0 010 $a3-540-49109-0 024 7 $a10.1007/3-540-58811-6 035 $a(CKB)1000000000234222 035 $a(SSID)ssj0000325695 035 $a(PQKBManifestationID)11225633 035 $a(PQKBTitleCode)TC0000325695 035 $a(PQKBWorkID)10264821 035 $a(PQKB)10894897 035 $a(DE-He213)978-3-540-49109-5 035 $a(PPN)155214500 035 $a(EXLCZ)991000000000234222 100 $a20121227d1994 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPlanning and Learning by Analogical Reasoning$b[electronic resource] /$fby Manuela M. Veloso 205 $a1st ed. 1994. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1994. 215 $a1 online resource (XIV, 190 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v886 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-58811-6 327 $aOverview -- The problem solver -- Generation of problem solving cases -- Case storage: Automated indexing -- Efficient case retrieval -- Analogical replay -- Empirical results -- Related work -- Conclusion. 330 $aThis research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity. In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners. 410 0$aLecture Notes in Artificial Intelligence ;$v886 606 $aArtificial intelligence 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aArtificial intelligence. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aArtificial Intelligence. 676 $a006.3/1 700 $aVeloso$b Manuela M$4aut$4http://id.loc.gov/vocabulary/relators/aut$0754972 906 $aBOOK 912 $a996466161203316 996 $aPlanning and learning by analogical reasoning$91519475 997 $aUNISA