Suchergebnisse - "inductive logic programming"

Pearce's Characterisation in an Epistemic Domain

Autoren: Su, Ezgi Iraz

Quelle: Electronic Proceedings in Theoretical Computer Science. 416:201-214

Schlagwörter: Programming problem, FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Literals, Computer Science - Programming Languages, Computer Science - Artificial Intelligence, Problem-solving, Answer set programming, Monotonics, Equilibrium logic, Answer set, Program debugging, Specification languages, Logic in Computer Science (cs.LO), Artificial Intelligence (cs.AI), Logic programs, Non-monotonic reasoning, Declarative logic, Inductive logic programming (ILP), Majority logic, Programming Languages (cs.PL)

Zugangs-URL: http://arxiv.org/abs/2502.09221
https://hdl.handle.net/11486/4356
https://doi.org/10.4204/EPTCS.416.18

Synthesizing Recursive Logic Programs by Inverting General Resolution

Autoren: Taosheng Qiu Ryutaro Ichise

Quelle: IEEE Access, Vol 13, Pp 102649-102658 (2025)

Schlagwörter: Inductive logic programming, Electrical engineering. Electronics. Nuclear engineering, inverse resolution, predicate invention, TK1-9971

Zugangs-URL: https://doaj.org/article/e78f644541064187840b6691444d44a4

Learning differentiable logic programs for abstract visual reasoning

Autoren: Hikaru Shindo Viktor Pfanschilling Devendra Singh Dhami et al.

Quelle: Machine Learning. 113:8533-8584

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Differentiable reasoning, Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Inductive logic programming, Computer Vision and Pattern Recognition, Neuro-symbolic AI, Graph neural network, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2307.00928

Automating Hypothesis Generation and Testing: Towards Self-driving Biology

Autoren: Brunnsåker, Daniel, 1992

Schlagwörter: mass spectrometry, systems biology, machine learning, laboratory automation, Automation of science, inductive logic programming, metabolomics

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/548360
https://research.chalmers.se/publication/548360/file/548360_Fulltext.pdf

Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

Autoren: FIEKE HILLERSTRÖM GERTJAN BURGHOUTS DSS - Defence, Safety & Security et al.

Weitere Verfasser: FIEKE HILLERSTRÖM GERTJAN BURGHOUTS DSS - Defence, Safety & Security et al.

Quelle: Theory and Practice of Logic Programming, 24(4), pp. 628-643.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Sensory data, Computer Science - Artificial Intelligence, Probabilistic background knowledge, Inductive logic programming, Neurosymbolic inference, Relational patterns, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2408.11367
https://publications.tno.nl/publication/34643722/h3r4B1wu/TNO-2025-P10140.pdf
https://resolver.tno.nl/uuid:b366fa16-c6b6-46b1-9fb7-c67b83ea456e

Learning answer set programs with aggregates via sampling and genetic programming

Autoren: Azzolini D.

Quelle: Machine Learning. 114

Schlagwörter: Answer set programming, Genetic programming, Inductive logic programming

Zugangs-URL: https://link.springer.com/article/10.1007/s10994-025-06780-7
https://doi.org/10.1007/s10994-025-06780-7
https://hdl.handle.net/11392/2590650

Counterfactual Explanations for Dynamic Multi-Valued Logic

Autoren: Ribeiro, Tony Folschette, Maxime Magnin, Morgan et al.

Weitere Verfasser: Ribeiro, Tony Folschette, Maxime Magnin, Morgan et al.

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], logical modeling, counterfactual explanations, inductive logic programming, Explainable artificial intelligence, dynamic systems

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05230575v1

Task-To-Processor Assignment for Real-Time Mixed-Critical Networked Systems Using Inductive Logic Programming

Autoren: Gualtieri, Marcus Juette, Christian Dasari, Dakshina et al.

Weitere Verfasser: Gualtieri, Marcus Juette, Christian Dasari, Dakshina et al.

Schlagwörter: Auto-Configuration, Task-to-Processor Mapping, Active Learning, ddc:004, Real-Time Distributed Systems, Inductive Logic Programming

Dateibeschreibung: application/pdf

Zugangs-URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.14

Online Inductive Learning from Answer Sets for Efficient Reinforcement Learning Exploration

Autoren: Veronese, Celeste Meli, Daniele Farinelli, Alessandro

Quelle: Communications in Computer and Information Science ISBN: 9783031893650

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Answer Set Programming, Explainable AI, Inductive Logic Programming, Neurosymbolic AI, Reinforcement Learning

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2501.07445
https://hdl.handle.net/11562/1164088
https://doi.org/10.1007/978-3-031-89366-7_7

Inductive learning of robot task knowledge from raw data and online expert feedback

Autoren: Meli, Daniele Fiorini, Paolo

Quelle: Machine Learning. 114

Schlagwörter: FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Computer Science - Artificial Intelligence, Answer set programming, Inductive logic programming, Learning under uncertainty, Logic in Computer Science (cs.LO), Computer Science - Robotics, Artificial Intelligence (cs.AI), Online learning, Symbolic planning, Explainable AI, Human-robot interaction, Robotics (cs.RO)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2501.07507

Agentic AI Integrated with Scientific Knowledge: Laboratory Validation in Systems Biology

Autoren: Brunnsåker, Daniel Gower, Alexander Naval, Prajakta et al.

Schlagwörter: systems biology, automation of science, inductive logic programming, machine learning, Laboratory automation, large language models

Zugangs-URL: https://research.chalmers.se/publication/548359

Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

Autoren: Hillerström, F.H.J. Burghouts, G.J. DSS - Defence, Safety & Security et al.

Weitere Verfasser: Hillerström, F.H.J. Burghouts, G.J. DSS - Defence, Safety & Security et al.

Quelle: Theory and Practice of Logic Programming, 24(4), pp. 628-643.

Schlagwörter: Sensory data, Probabilistic background knowledge, Inductive logic programming, Neurosymbolic inference, Relational patterns

Zugangs-URL: https://publications.tno.nl/publication/34643722/h3r4B1wu/TNO-2025-P10140.pdf
https://resolver.tno.nl/uuid:b366fa16-c6b6-46b1-9fb7-c67b83ea456e

MP-SPILDL: A Massively Parallel Inductive Logic Learner in Description Logic

Autoren: Eyad Algahtani

Quelle: IEEE Access, Vol 12, Pp 130884-130895 (2024)

Schlagwörter: big data, Machine learning, GPU, inductive logic programming, description logic, MapReduce, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/d43473e2e4474178882045687413fe83

MP-HTHEDL: A Massively Parallel Hypothesis Evaluation Engine in Description Logic

Autoren: Eyad Algahtani

Quelle: IEEE Access, Vol 12, Pp 89113-89123 (2024)

Schlagwörter: big data, Machine learning, GPU, 0202 electrical engineering, electronic engineering, information engineering, inductive logic programming, description logic, MapReduce, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Zugangs-URL: https://doaj.org/article/25dbc1dd646a41fb9826342a8e6be4fb

Explainable AI for Sperm Morphology: Integrating YOLO with FastLAS

Autoren: Dreossi T. Dovier A. Urli S. et al.

Weitere Verfasser: Dreossi T. Dovier A. Urli S. et al.

Schlagwörter: AI Explainability, ASP, Inductive Logic Programming, Object Detection

Relation: ispartofbook:CEUR Workshop Proceedings; 40th Italian Conference on Computational Logic, CILC 2025; volume:4003; serie:CEUR WORKSHOP PROCEEDINGS; https://hdl.handle.net/11390/1312465

Verfügbarkeit: https://hdl.handle.net/11390/1312465

Learning Brave Assumption-Based Argumentation Frameworks via ASP

Autoren: Emanuele De Angelis Maurizio Proietti Francesca Toni

Quelle: Frontiers in Artificial Intelligence and Applications ISBN: 9781643685489
32nd International Conference on Inductive Logic Programming, Bari, 13-15/11/2023
info:cnr-pdr/source/autori:Emanuele De Angelis; Maurizio Proietti; Francesca Toni/congresso_nome:32nd International Conference on Inductive Logic Programming/congresso_luogo:Bari/congresso_data:13-15%2F11%2F2023/anno:2023/pagina_da:/pagina_a:/intervallo_pagine

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Logic in Computer Science, Logic-based Learning, Answer Set Programming, Computer Science - Artificial Intelligence, Logic-based machine learning, Computational Argumentation, Inductive logic programming, Symbolic Machine Learning, Machine Learning (cs.LG), Logic in Computer Science (cs.LO), Artificial Intelligence (cs.AI), Assumption-Based Argumentation

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2408.10126
https://ilp2023.unife.it/wp-content/uploads/2023/11/Learning-Brave-Assumption-Based-Argumentation-Frameworks-via-ASP_De_Angelis_Proietti_Toni.pdf
https://publications.cnr.it/doc/489123
http://www.cnr.it/prodotto/i/489123

Proxy Attribute Discovery in Machine Learning Datasets via Inductive Logic Programming (Extended Version)

Autoren: Goncalves, Rafael Gouveia, Filipe Lynce, Ines et al.

Schlagwörter: Machine Learning, Proxy Attributes, Fairness, Inductive Logic Programming

Object Identity Reloaded—A Comprehensive Reference for an Efficient and Effective Framework for Logic-Based Machine Learning

Autoren: Stefano Ferilli

Quelle: Electronics ; Volume 14 ; Issue 8 ; Pages: 1523

Schlagwörter: machine learing, inductive logic programming, incrementality, object identity

Dateibeschreibung: application/pdf

Relation: Computer Science & Engineering; https://dx.doi.org/10.3390/electronics14081523

Verfügbarkeit: https://doi.org/10.3390/electronics14081523

Relational data factorization

Autoren: Paramonov, Sergey van Leeuwen, Matthijs De Raedt, Luc

Quelle: Machine Learning. 106(12):1867-1904

Schlagwörter: Answer set programming, Inductive logic programming, Pattern mining, Relational data, Factorization, Data mining, Declarative modeling

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-84427
https://doi.org/10.1007/s10994-017-5660-6

Inductive Programming Meets the Real World.

Autoren: GULWANI, SUMIT HERNÁNDEZ-ORALLO, JOSÉ KITZELMANN, EMANUEL et al.

Quelle: Communications of the ACM. Nov2015, Vol. 58 Issue 11, p90-99. 10p. 1 Color Photograph, 2 Diagrams, 7 Charts.

Schlagwörter: *APPLICATION software, *ELECTRONIC spreadsheets, *SCRIPTING languages (Computer science), INDUCTIVE logic programming, END users (Information technology), INTELLIGENT tutoring systems