Learning Through Hypothesis Refinement Using Answer Set Programming

Foundations of Logic Programming

Lloyd, J. (1984) · Symbolic Computation · doi:10.1007/978-3-642-96826-6 · Cited by 5,772 · see citing works

Logic Programming

Apt, K. R. (1992) · Cited by 1,678 · see citing works

Inductive Logic Programming: Theory and methods

Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537 · see citing works

Knowledge Representation, Reasoning and Declarative Problem Solving

Baral, C. R. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511543357 · Cited by 1,443 · see citing works

The DLV system for knowledge representation and reasoning

Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., & Scarcello, F. (2006) · ACM Transactions on Computational Logic · doi:10.1145/1149114.1149117 · Cited by 1,152 · see citing works

Potassco: The Potsdam Answer Set Solving Collection

Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., & Schneider, M. (2011) · AI Communications · doi:10.3233/aic-2011-0491 · Cited by 602 · see citing works

Logic Programming and Nonmonotonic Reasoning

Delgrande, J., & Faber, W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20895-9 · Cited by 320 · see citing works

Advances in Inductive Logic Programming

Raedt, L. D. (1996) · Cited by 313 · see citing works

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184 · see citing works

Nonmonotonic abductive inductive learning

Ray, O. (2008) · Journal of Applied Logic · doi:10.1016/j.jal.2008.10.007 · Cited by 147 · see citing works

Inductive Logic Programming in Answer Set Programming

Corapi, D., Russo, A., & Lupu, E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31951-8_12 · Cited by 69 · see citing works

Special Issue on Answer Set Programming

Schaub, T. H., & Woltran, S. (2018) · Künstliche Intell. · doi:10.1007/s13218-018-0554-8 · Cited by 63 · see citing works

Incremental learning of event definitions with Inductive Logic Programming

Katzouris, N., Artikis, A., & Paliouras, G. (2015) · Machine Learning · doi:10.1007/s10994-015-5512-1 · Cited by 62 · see citing works

Machine Learning: ECML-95

(1995) · Lecture notes in computer science · doi:10.1007/3-540-59286-5 · Cited by 62 · see citing works

Inductive Logic Programming as Abductive Search

Corapi, D., Russo, A., & Lupu, E. (2010) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.iclp.2010.54 · Cited by 60 · see citing works

Induction from answer sets in nonmonotonic logic programs

Sakama, C. (2005) · ACM Transactions on Computational Logic · doi:10.1145/1055686.1055687 · Cited by 55 · see citing works

Brave induction: a logical framework for learning from incomplete information

Sakama, C., & Inoue, K. (2009) · Machine Learning · doi:10.1007/s10994-009-5113-y · Cited by 45 · see citing works

Learning non-monotonic logic programs: Learning exceptions

Dimopoulos, Y., & Kakas, A. C. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59286-5_53 · Cited by 41 · see citing works

Refining Complete Hypotheses in ILP

Ivan Bratko, A. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4_6 · Cited by 40 · see citing works

Technical Communications of the 26th International Conference on Logic Programming

Janhunen, T. (2010) · Cited by 35 · see citing works

Normative design using inductive learning

Corapi, D., Russo, A., Vos, M. D., Padget, J. A., & Satoh, K. (2011) · Theory and Practice of Logic Programming · doi:10.1017/s1471068411000305 · Cited by 33 · see citing works

TopLog: ILP Using a Logic Program Declarative Bias

Muggleton, S. H., Santos, J. C. A., & Tamaddoni-Nezhad, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-89982-2_58 · Cited by 29 · see citing works

Special issue on answer set programming

Balduccini, M., & Woltran, S. (2011) · AI Communications · doi:10.3233/aic-2011-0497 · Cited by 27 · see citing works

Predicate Invention in Inductive Logic Programming

Athakravi, D., Broda, K. B., & Russo, A. (2012) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.iccsw.2012.15 · Cited by 24 · see citing works

Nonmonotonic Inductive Logic Programming

Sakama, C. (2001) · Cited by 21 · see citing works

Induction on Failure: Learning Connected Horn Theories

Kimber, T., Broda, K. B., & Russo, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04238-6_16 · Cited by 18 · see citing works

Introduction to the special issue on learning and computational game theory

Greenwald, A., & Littman, M. L. (2007) · Machine Learning · doi:10.1007/s10994-007-0770-1 · Cited by 17 · see citing works

Nonmonotonic Inductive Logic Programming as Abductive Search

Corapi, D. (2011) · Spiral (Imperial College London) · doi:10.25560/9814 · Cited by 9 · see citing works

Learning Definite and Normal Logic Programs by Induction on Failure

Kimber, T. (2011) · Spiral (Imperial College London) · doi:10.25560/9961 · Cited by 3 · see citing works

Learning programs by learning from failures

Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works

Inductive Logic Programming At 30: A New Introduction

Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works

Turning 30: New Ideas in Inductive Logic Programming

Cropper, A., Dumančić, S., & Muggleton, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/673 · Cited by 78 · see citing works

Incremental learning of event definitions with Inductive Logic Programming

Katzouris, N., Artikis, A., & Paliouras, G. (2015) · Machine Learning · doi:10.1007/s10994-015-5512-1 · Cited by 62 · see citing works

The complexity and generality of learning answer set programs

Law, M., Russo, A., & Broda, K. B. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.03.005 · Cited by 43 · see citing works

Adapting requirements models to varying environments

Alrajeh, D., Cailliau, A., & van Lamsweerde, A. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380927 · Cited by 35 · see citing works

Privacy dynamics

Çalıklı, G., Law, M., Bandara, A. K., Russo, A., Dickens, L., Price, B. A., Stuart, A., Levine, M., & Nuseibeh, B. (2016) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1145/2897053.2897063 · Cited by 29 · see citing works

The ILASP system for Inductive Learning of Answer Set ProgramsPreprint

Law, M., Russo, A., & Broda, K. B. (2020) · arXiv (Cornell University) · doi:10.4230/oasics.rw.2024/2025.8 · Cited by 22 · see citing works

Best-effort inductive logic programming via fine-grained cost-based hypothesis generation: The inspire system at the inductive logic programming competition

Schüller, P., & Benz, M. (2018) · Machine Learning · doi:10.1007/s10994-018-5708-2 · Cited by 21 · see citing works

Logic-Based Learning of Answer Set Programs

Law, M., Russo, A., & Broda, K. B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31423-1_6 · Cited by 21 · see citing works

Conflict-Driven Inductive Logic Programming

Law, M. (2022) · Theory and Practice of Logic Programming · doi:10.1017/s1471068422000011 · Cited by 20 · see citing works

Event Recognition Challenges and Techniques

Artikis, A., Gal, A., Kalogeraki, V., & Weidlich, M. (2014) · ACM Transactions on Internet Technology · doi:10.1145/2632220 · Cited by 18 · see citing works

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Katzouris, N., Παλιούρας, Γ., & Artikis, A. (2021) · Theory and Practice of Logic Programming · doi:10.1017/s1471068421000107 · Cited by 13 · see citing works

Online Learning of Weighted Relational Rules for Complex Event Recognition

Katzouris, N., Michelioudakis, E., Artikis, A., & Παλιούρας, Γ. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10928-8_24 · Cited by 12 · see citing works

Risk-driven revision of requirements models

Alrajeh, D., van Lamsweerde, A., Kramer, J., Russo, A., & Uchitel, S. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884838 · Cited by 11 · see citing works

Logic Programming and Nonmonotonic Reasoning

(2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15707-3 · Cited by 10 · see citing works

Iterative Learning of Answer Set Programs from Context Dependent Examples

LAW, M., RUSSO, A., & BRODA, K. (2016) · Theory and Practice of Logic Programming · doi:10.1017/s1471068416000351 · Cited by 8 · see citing works

WOLED: A tool for Online Learning Weighted Answer Set Rules for Temporal Reasoning Under Uncertainty

Katzouris, N., & Artikis, A. (2020) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2020/81 · Cited by 8 · see citing works

Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Mitra, A., & Baral, C. R. (2018) · Theory and Practice of Logic Programming · doi:10.1017/s1471068418000248 · Cited by 7 · see citing works

Top program construction and reduction for polynomial time Meta-Interpretive learning

Patsantzis, S., & Muggleton, S. (2021) · Machine Learning · doi:10.1007/s10994-020-05945-w · Cited by 7 · see citing works

From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems

Bembenek, A., Greenberg, M., & Chong, S. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3571200 · Cited by 6 · see citing works

Learning answer set programs with aggregates via sampling and genetic programming

Azzolini, D. (2025) · Machine Learning · doi:10.1007/s10994-025-06780-7 · Cited by 3 · see citing works

Inductive Logic Programming for Explainable Graph Clustering

Schramm, S., & Schmid, U. (2023) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg59574.2023.00034

Inductive logic programming using bounded hypothesis space

Athakravi, D. (2015) · Spiral (Imperial College London) · doi:10.25560/33798 · Cited by 4 · see citing works

Inductive Logic Programming in Answer Set Programming

Corapi, D., Russo, A., & Lupu, E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31951-8_12 · Cited by 69 · see citing works

Learning weak constraints in answer set programming

LAW, M., RUSSO, A., & BRODA, K. (2015) · Theory and Practice of Logic Programming · doi:10.1017/s1471068415000198 · Cited by 33 · see citing works

Inductive Learning of Answer Set Programs

Law, M., Russo, A., & Broda, K. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11558-0_22 · Cited by 133 · see citing works

Iterative Learning of Answer Set Programs from Context Dependent Examples

LAW, M., RUSSO, A., & BRODA, K. (2016) · Theory and Practice of Logic Programming · doi:10.1017/s1471068416000351 · Cited by 8 · see citing works