LEADER 05225nam 22008535 450 001 9910484298903321 005 20251007120444.0 010 $a1-280-38580-4 010 $a9786613563729 010 $a3-642-11931-X 024 7 $a10.1007/978-3-642-11931-6 035 $a(CKB)2670000000010114 035 $a(SSID)ssj0000399590 035 $a(PQKBManifestationID)11279265 035 $a(PQKBTitleCode)TC0000399590 035 $a(PQKBWorkID)10375787 035 $a(PQKB)10590379 035 $a(DE-He213)978-3-642-11931-6 035 $a(MiAaPQ)EBC3065168 035 $a(PPN)149059736 035 $a(BIP)29122085 035 $a(EXLCZ)992670000000010114 100 $a20100325d2010 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aApproaches and Applications of Inductive Programming $eThird International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009, Revised Papers /$fedited by Ute Schmid, Emanuel Kitzelmann 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (IX, 195 p. 14 illus.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v5812 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-11930-1 320 $aIncludes bibliographical references and index. 327 $aInvited Papers -- Deriving a Relationship from a Single Example -- Synthesis of Functions Using Generic Programming -- Regular Papers -- Inductive Programming: A Survey of Program Synthesis Techniques -- Incremental Learning in Inductive Programming -- Enumerating Well-Typed Terms Generically -- Generalisation Operators for Lists Embedded in a Metric Space -- Porting IgorII from Maude to Haskell -- Automated Method Induction: Functional Goes Object Oriented -- Recent Improvements of MagicHaskeller. 330 $aInductive programming is concerned with the automated construction of decl- ative-often functional -recursiveprogramsfromincompletespeci'cationssuch as input/output examples. The inferred program must be correct with respect to the provided examples in a generalizing sense: it should be neither equivalent to it, nor inconsistent. Inductive programming algorithms are guided explicitly or implicitly by a language bias (the class of programs that can be induced) and a search bias (determining which generalized program is constructed ?rst). Induction strategiesare either generate-and-testor example-driven.In genera- and-test approaches, hypotheses about candidate programs are generated in- pendently from the given speci'cations. Program candidates are tested against the given speci'cation and one or more of the best evaluated candidates are - veloped further. In analytical approaches, candidate programs are constructed in an example-driven way. While generate-and-test approaches can - in prin- ple - construct any kind of program, analytical approaches have a more limited scope. On the other hand, e'ciency of induction is much higher in analytical approaches. Inductive programming is still mainly a topic of basic research, exploring how the intellectual ability of humans to infer generalized recursive procedures from incomplete evidence can be captured in the form of synthesis methods. Intended applications are mainly in the domain of programming assistance - either to relieve professional programmers from routine tasks or to enable n- programmers to some limited form of end-user programming. Furthermore, in future,inductiveprogrammingtechniquesmightbe appliedtofurtherareassuch as support inference of lemmata in theorem proving or learning grammar rules. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v5812 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aMachine theory 606 $aComputer science 606 $aApplication software 606 $aComputer programming 606 $aSoftware Engineering 606 $aArtificial Intelligence 606 $aFormal Languages and Automata Theory 606 $aComputer Science Logic and Foundations of Programming 606 $aComputer and Information Systems Applications 606 $aProgramming Techniques 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 0$aMachine theory. 615 0$aComputer science. 615 0$aApplication software. 615 0$aComputer programming. 615 14$aSoftware Engineering. 615 24$aArtificial Intelligence. 615 24$aFormal Languages and Automata Theory. 615 24$aComputer Science Logic and Foundations of Programming. 615 24$aComputer and Information Systems Applications. 615 24$aProgramming Techniques. 676 $a005.1 701 $aSchmid$b U$g(Ute)$0564613 701 $aKitzelmann$b Emanuel$01750512 701 $aPlasmeijer$b M. J$g(Marinus Jacobus)$01750513 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484298903321 996 $aApproaches and applications of inductive programming$94185158 997 $aUNINA