國立虎尾科技大學 |

Inductive Logic Programming = 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Inductive Logic Programming/ edited by Nikos Katzouris, Alexander Artikis.
其他題名:	30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings /
其他作者:	Katzouris, Nikos.
面頁冊數:	X, 283 p. 61 illus., 40 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-97454-1
ISBN:	9783030974541

Inductive Logic Programming = 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings /
Inductive Logic Programming30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings /[electronic resource] :edited by Nikos Katzouris, Alexander Artikis. - 1st ed. 2022. - X, 283 p. 61 illus., 40 illus. in color.online resource. - Lecture Notes in Artificial Intelligence,131912945-9141 ;. - Lecture Notes in Artificial Intelligence,13406.

Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge -- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference -- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation -- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification -- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning -- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design -- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem -- Ontology Graph Embeddings and ILP for Financial Forecasting -- Transfer learning for boosted relational dependency networks through genetic algorithm -- Online Learning of Logic Based Neural Network Structures -- Programmatic policy extraction by iterative local search -- Mapping across relational domains for transfer learning with word embeddings-based similarity -- A First Step Towards Even More Sparse Encodings of Probability Distributions -- Feature Learning by Least Generalization -- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance -- Learning and revising dynamic temporal theories in the full Discrete Event Calculus -- Human-like rule learning from images using one-shot hypothesis derivation -- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits -- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics. .

This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

ISBN: 9783030974541

Standard No.: 10.1007/978-3-030-97454-1doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Inductive Logic Programming = 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings /
LDR:03812nam a22004215i 4500 001 1094796
003 DE-He213
005 20221012193645.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030974541 $9 978-3-030-97454-1
024 7 $a 10.1007/978-3-030-97454-1 $2 doi
035 $a 978-3-030-97454-1
050 4 $a Q334-342
050 4 $a TA347.A78
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Inductive Logic Programming $h [electronic resource] : $b 30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings / $c edited by Nikos Katzouris, Alexander Artikis.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a X, 283 p. 61 illus., 40 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Artificial Intelligence, $x 2945-9141 ; $v 13191
505 0 $a Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge -- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference -- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation -- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification -- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning -- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design -- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem -- Ontology Graph Embeddings and ILP for Financial Forecasting -- Transfer learning for boosted relational dependency networks through genetic algorithm -- Online Learning of Logic Based Neural Network Structures -- Programmatic policy extraction by iterative local search -- Mapping across relational domains for transfer learning with word embeddings-based similarity -- A First Step Towards Even More Sparse Encodings of Probability Distributions -- Feature Learning by Least Generalization -- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance -- Learning and revising dynamic temporal theories in the full Discrete Event Calculus -- Human-like rule learning from images using one-shot hypothesis derivation -- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits -- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics. .
520 $a This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computer engineering. $3 569006
650 0 $a Computer networks . $3 1365720
650 0 $a Compilers (Computer programs). $3 975050
650 0 $a Computer science. $3 573171
650 0 $a Machine theory. $3 527775
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Computer Engineering and Networks. $3 1365733
650 2 4 $a Compilers and Interpreters. $3 1365748
650 2 4 $a Computer Science Logic and Foundations of Programming. $3 1365757
650 2 4 $a Formal Languages and Automata Theory. $3 1365747
700 1 $a Katzouris, Nikos. $e editor. $1 https://orcid.org/0000-0001-8804-470X $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1402986
700 1 $a Artikis, Alexander. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 682701
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030974534
776 0 8 $i Printed edition: $z 9783030974558
830 0 $a Lecture Notes in Artificial Intelligence, $x 2945-9141 ; $v 13406 $3 1387736
856 4 0 $u https://doi.org/10.1007/978-3-030-97454-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)