LEADER 03594nam 22004935 450 001 996465827703316 005 20200705100207.0 010 $a3-540-47739-X 024 7 $a10.1007/3-540-18081-8 035 $a(CKB)1000000000230646 035 $a(SSID)ssj0000321247 035 $a(PQKBManifestationID)11256254 035 $a(PQKBTitleCode)TC0000321247 035 $a(PQKBWorkID)10263102 035 $a(PQKB)11166064 035 $a(DE-He213)978-3-540-47739-6 035 $a(PPN)155235028 035 $a(EXLCZ)991000000000230646 100 $a20121227d1987 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAnalogical and Inductive Inference$b[electronic resource] $eInternational Workshop AII'86 Wendisch-Rietz, GDR, October 6-10, 1986, Proceedings /$fedited by Klaus P. Jantke 205 $a1st ed. 1987. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1987. 215 $a1 online resource (VIII, 232 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v265 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-18081-8 327 $aTowards the development of an analysis of learning algorithms -- Using the algorithm of analogy for generation of robot programs -- On the inference of sequences of functions -- Fixed point equations as hypotheses in inductive reasoning -- Inductive inference of functions from noised observations -- Reasoning by analogy as a partial identity between models -- Can missing information be also useful? -- A decidability problem of church-rosser specifications for program synthesis -- Some considerations about formalization of analogical reasoning -- Analogical reasoning using graph transformations -- Knowledge acquisition by inductive learning from examples -- On the inference of programs approximately computing the desired function -- Stratified inductive hypothesis generation -- A model theoretic oriented approach to analogy -- On the complexity of effective program synthesis -- On barzdin's conjecture. 330 $aThis volume contains revised versions of presentations at the International Workshop on Analogical and Inductive Inference (AII '86) held in Wendisch-Rietz, GDR, October 16-10, 1986. Inductive inference and analogical reasoning are two basic approaches to learning algorithms. Both allow for exciting problems and promising concepts of invoking deeper mathematical results for considerable advances in intelligent software systems. Hence analogical and inductive inference may be understood as a firm mathematical basis for a large variety of problems in artificial intelligence. While the papers on inductive inference contain technical results and reflect the state of the art of this well-developed mathematical theory, those devoted to analogical reasoning reflect the ongoing process of developing the basic concepts of the approach. The workshop thus contributes significantly to the advancement of this field. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v265 606 $aArtificial intelligence 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 702 $aJantke$b Klaus P$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996465827703316 996 $aAnalogical and Inductive Inference$92829764 997 $aUNISA