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

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (293 pages)
• Disciplina: 005.115
• Soggetto topico: 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
• Persona (resp. second.): KatzourisNikos; ArtikisAlexander
• Note generali: Includes index.
• Nota di contenuto: 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. .
• Sommario/riassunto: 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.
• Titolo autorizzato: Inductive Logic Programming
• ISBN: 3-030-97454-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910552749203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 13191