Inductive Logic Programming : 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings / edited by Nikos Katzouris, Alexander Artikis.

Format:

Book

Contributor:

Katzouris, Nikos., Editor.

Artikis, Alexander, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 13191

Lecture Notes in Artificial Intelligence, 2945-9141 ; 13191

Language:

English

Subjects (All):

Artificial intelligence.

Computer engineering.

Computer networks.

Compilers (Computer programs).

Computer science.

Machine theory.

Artificial Intelligence.

Computer Engineering and Networks.

Compilers and Interpreters.

Computer Science Logic and Foundations of Programming.

Formal Languages and Automata Theory.

Local Subjects:

Artificial Intelligence.

Computer Engineering and Networks.

Compilers and Interpreters.

Computer Science Logic and Foundations of Programming.

Formal Languages and Automata Theory.

Physical Description:

1 online resource (X, 283 pages) : 61 illustrations, 40 illustrations in color.

Edition:

1st edition 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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. .

Other Format:

Printed edition:

ISBN:

978-3-030-97454-1

9783030974541

Access Restriction:

Restricted for use by site license.