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Inductive logic programming30th International Conference, ILP 2021, virtual event, October 25-27, 2021 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inductive logic programmingedited by Nikos Katzouris, Alexander Artikis.
Reminder of title:	30th International Conference, ILP 2021, virtual event, October 25-27, 2021 : proceedings /
remainder title:	ILP 2021
other author:	Katzouris, Nikos.
corporate name:
Published:	Cham :Springer International Publishing :2022.
Description:	x, 283 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Logic programming
Online resource:	https://doi.org/10.1007/978-3-030-97454-1
ISBN:	9783030974541$q(electronic bk.)

Inductive logic programming30th 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] :ILP 2021edited by Nikos Katzouris, Alexander Artikis. - Cham :Springer International Publishing :2022. - x, 283 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,131910302-9743 ;. - Lecture notes in computer science ;4891..

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 2032, 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$q(electronic bk.)

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

295961
Logic programming

LC Class. No.: QA76.63 / .I47 2021

Dewey Class. No.: 005.115

Inductive logic programming30th International Conference, ILP 2021, virtual event, October 25-27, 2021 : proceedings /
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520 $a This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2032, 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.
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