000			04662nam a22006255i 4500
001			978-3-030-97454-1
003			DE-He213
005			20240423125531.0
007			cr nn 008mamaa
008			220223s2022 sz | s |||| 0|eng d
020			_a9783030974541 _9978-3-030-97454-1
024	7		_a10.1007/978-3-030-97454-1 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
245	1	0	_aInductive Logic Programming _h[electronic resource] : _b30th International Conference, ILP 2021, Virtual Event, October 25–27, 2021, Proceedings / _cedited by Nikos Katzouris, Alexander Artikis.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_aX, 283 p. 61 illus., 40 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		_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v13191
505	0		_aEmbedding 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			_aThis 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	_aArtificial intelligence.
650		0	_aComputer engineering.
650		0	_aComputer networks .
650		0	_aCompilers (Computer programs).
650		0	_aComputer science.
650		0	_aMachine theory.
650	1	4	_aArtificial Intelligence.
650	2	4	_aComputer Engineering and Networks.
650	2	4	_aCompilers and Interpreters.
650	2	4	_aComputer Science Logic and Foundations of Programming.
650	2	4	_aFormal Languages and Automata Theory.
700	1		_aKatzouris, Nikos. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aArtikis, Alexander. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030974534
776	0	8	_iPrinted edition: _z9783030974558
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v13191
856	4	0	_uhttps://doi.org/10.1007/978-3-030-97454-1
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
912			_aZDB-2-LNC
942			_cSPRINGER
999			_c178981 _d178981