Incremental learning of event definitions with Inductive Logic Programming
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
If learning to recognise events, a user could provide the axioms of the event calculus.
“If learning to recognise events, a user could provide the axioms of the event calculus (Katzouris et al., 2015).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Katzouris et al., 2015
Because of the expressivity of logic programs, ILP can learn complex relational theories, such as cellular automata, event calculus theories, and Petri nets, and various forms of non-monotonic programs.
“Because of the expressivity of logic programs, ILP can learn complex relational theories, such as cellular automata (Inoue et al., 2014; Evans et al., 2021), event calculus theories (Katzouris et al., 2015), and Petri nets (Bain & Srinivasan, 2018), and various forms of non-monotonic programs (Bain & Muggleton, 1991; Inoue & Kudoh, 1997; Sakama, 2001).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Katzouris et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Foundations of Logic Programming
Lloyd, J. (1984) · Symbolic Computation · doi:10.1007/978-3-642-96826-6 · Cited by 5,772 · see citing works
(1988) · Cited by 3,452 · see citing works
Fundamenta informaticae 34(3) 1998
Apt, K. R., & Kirchner, C. (1998) · Data Archiving and Networked Services (DANS) · Cited by 3,011 · see citing works
Foundations of Logic Programming
Lloyd, J. W. (1987) · Springer eBooks · doi:10.1007/978-3-642-83189-8 · Cited by 2,736 · see citing works
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644 · 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
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411 · see citing works
A logic-based calculus of events
Kowalski, R., & Sergot, M. (1989) · New generation computing · doi:10.1007/bf03037383 · Cited by 1,393 · see citing works
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
Luckham, D. C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88808-6_2 · Cited by 1,291 · see citing works
Selective Sampling Using the Query by Committee Algorithm
Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997) · Machine Learning · doi:10.1023/a:1007330508534 · Cited by 1,098 · see citing works
Logic programs with stable model semantics as a constraint programming paradigm
Niemelä, I. (1999) · Annals of Mathematics and Artificial Intelligence · doi:10.1023/a:1018930122475 · Cited by 837 · see citing works
Kakas, A. C., KOWALSKI, R. A., & Toni, F. (1992) · Journal of Logic and Computation · doi:10.1093/logcom/2.6.719 · Cited by 833 · see citing works
Inductive Logic Programming: Techniques and Applications
Lavrač, N., & Džeroski, S. (1993) · Cited by 766 · see citing works
Etzion, O., & Niblett, P. (2010) · Cited by 622 · see citing works
Version spaces: an approach to concept learning.
Mitchell, T. M. (1979) · Defense Technical Information Center (DTIC) · Cited by 351 · see citing works
Answer Set Solving in Practice
Gebser, M., Kaminski, R., Kaufmann, B., & Schaub, T. H. (2012) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00457ed1v01y201211aim019 · Cited by 325 · see citing works
Abduction Compared with Negation by Failure.
Eshghi, K., & Kowalski, R. (1989) · Cited by 277 · see citing works
Mueller, E. T. (2006) · Cited by 240 · see citing works
Mining frequent itemsets over data streams using efficient window sliding techniques
Li, H.-F., & Lee, S.-Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.11.061 · Cited by 179 · see citing works
Generalized stable models: a semantics for abduction
Kakas, A. C., & Mancarella, P. M. (1990) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 175 · see citing works
Abduction in Logic Programming
Denecker, M., & Kakas, A. C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45628-7_16 · Cited by 173 · 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
Automated Refinement of First-Order Horn-Clause Domain Theories
Richards, B. L., & Mooney, R. J. (1995) · Machine Learning · doi:10.1023/a:1022611224557 · Cited by 138 · see citing works
ILP turns 20: Biography and future challenges
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126 · see citing works
Meta-interpretive learning: application to grammatical inference
Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A. (2014) · Machine Learning · doi:10.1007/s10994-013-5358-3 · Cited by 120 · see citing works
An Event Calculus for Event Recognition
Artikis, A., Sergot, M. J., & Παλιούρας, Γ. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2356476 · Cited by 117 · see citing works
Self-citationStructured machine learning: the next ten years
Dietterich, T. G., Domingos, P., Getoor, L. C., Muggleton, S., & Tadepalli, P. V. (2008) · Machine Learning · doi:10.1007/s10994-008-5079-1 · Cited by 108 · see citing works
An Efficient Algorithm for Mining Frequent Itemests over the Entire History of Data Streams
Li, H.-F., Lee, S.-Y., & Shan, M.-K. (2004) · Cited by 105 · see citing works
Theory Completion Using Inverse Entailment
Muggleton, S., & Bryant, C. H. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44960-4_8 · Cited by 101 · see citing works
An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams
沈錳坤 (2004) · European conference on Machine Learning · Cited by 98 · see citing works
(2006) · Elsevier eBooks · doi:10.1016/b978-0-12-369388-4.x5054-1 · Cited by 95 · see citing works
Artikis, A., Skarlatidis, A., Portet, F., & Paliouras, G. (2012) · Knowledge engineering review (Print) · doi:10.1017/s0269888912000264 · Cited by 80 · see citing works
Self-citationLearning operational requirements from goal models
Alrajeh, D., Kramer, J., Russo, A., & Uchitel, S. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2009.5070527 · Cited by 78 · see citing works
Multistrategy Theory Revision: Induction and Abduction in INTHELEX
Esposito, F., Semeraro, G. M., Fanizzi, N., & Ferilli, S. (2000) · Machine Learning · doi:10.1023/a:1007638124237 · Cited by 78 · 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
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
Paschke, A. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0610167 · Cited by 56 · 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
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Maloberti, J., & Sébag, M. (2004) · Machine Learning · doi:10.1023/b:mach.0000023150.80092.40 · Cited by 52 · see citing works
Richards, B. L., & Mooney, R. J. (1991) · Elsevier eBooks · doi:10.1016/b978-1-55860-200-7.50092-1 · Cited by 51 · see citing works
Hybrid Abductive Inductive Learning: A Generalisation of Progol
Ray, O., Broda, K. B., & Russo, A. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39917-9_21 · Cited by 48 · see citing works
Performance Evaluating the Evaluator
List, T., Bins, J., Vazquez, J., & Fisher, R. B. (2006) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/vspets.2005.1570907 · Cited by 43 · see citing works
Raedt, L. D., & Bruynooghe, M. (1994) · Lirias (KU Leuven) · Cited by 39 · see citing works
Extending the event calculus for tracking epidemic spread
Chaudet, H. (2006) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2005.06.001 · Cited by 38 · see citing works
Avoiding Order Effects in Incremental Learning
Mauro, N. D., Esposito, F., Ferilli, S., et al. (2005) · Lecture notes in computer science · doi:10.1007/11558590_12 · Cited by 38 · see citing works
A Methodology for Evaluating Theory Revision Systems: Results with Audrey II.
Wogulis, J., & Pazzani, M. J. (1993) · Cited by 37 · see citing works
Wrobel, S. (1996) · Cited by 36 · see citing works
Mueller, E. T. (2008) · Foundations of artificial intelligence · doi:10.1016/s1574-6526(07)03017-9 · Cited by 32 · see citing works
A calculus of macro-events: progress report
Iliano Cervesato, A., & Montanari, A. (2002) · doi:10.1109/time.2000.856584 · Cited by 31 · see citing works
ProLogICA : a practical system for Abductive Logic Programming
Ray, O., Kakas, A. C., Dix, J., et al. (2006) · Cited by 31 · see citing works
Engineering Policy-Based Ubiquitous Systems
Sloman, M. S., & Lupu, E. (2009) · The Computer Journal · doi:10.1093/comjnl/bxp102 · Cited by 30 · see citing works
Otero, R. P. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44797-0_16 · Cited by 27 · see citing works
Learning Through Hypothesis Refinement Using Answer Set Programming
Athakravi, D., Corapi, D., Broda, K., & Russo, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-44923-3_3 · Cited by 25 · see citing works
Learning Rules from User Behaviour
Corapi, D., Ray, O., Russo, A., Bandara, A. K., & Lupu, E. (2009) · IFIP advances in information and communication technology · doi:10.1007/978-1-4419-0221-4_54 · Cited by 24 · see citing works
Incremental learning and concept drift in INTHELEX
Esposito, F., Ferilli, S., Fanizzi, N., et al. (2004) · Intelligent Data Analysis · doi:10.3233/ida-2004-8302 · Cited by 22 · see citing works
Induction of the Effects of Actions by Monotonic Methods
Otero, R. P. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39917-9_20 · Cited by 21 · see citing works
BEHAVIOUR RECOGNITION FROM VIDEO CONTENT: A LOGIC PROGRAMMING APPROACH
Artikis, A., Skarlatidis, A., & Παλιούρας, Γ. (2010) · International Journal of Artificial Intelligence Tools · doi:10.1142/s021821301000011x · Cited by 20 · see citing works
Self-citationAILP abductive inductive logic programming
Adé, H., & Denecker, M. (1995) · Cited by 20 · see citing works
Luckham, D. C. (2012) · doi:10.1002/9781119198697.app1 · Cited by 20 · 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
Incremental Declarative Process Mining
Cattafi, M., Lamma, E., Riguzzi, F., et al. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-04584-4_5 · Cited by 17 · see citing works
Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples
Muggleton, S., Paes, A., Costa, V. S., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13840-9_12 · Cited by 15 · see citing works
Inverse Entailment in Nonmonotonic Logic Programs
Sakama, C. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44960-4_13 · Cited by 15 · see citing works
Learning from Vacuously Satisfiable Scenario-Based Specifications
Alrajeh, D., Kramer, J., Russo, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28872-2_26 · Cited by 14 · see citing works
A Restarted Strategy for Efficient Subsumption Testing
Kuželka, O., & Železný, F. (2009) · Cited by 14 · see citing works
An investigation into theory completion techniques in inductive logic programming
Moyle, S. (2003) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.5287/ora-dx0r5jop5 · Cited by 14 · see citing works
Revising Process Models through Inductive Learning
Maggi, F. M., Corapi, D., Russo, A., Lupu, E., & Visaggio, G. (2011) · Lecture notes in business information processing · doi:10.1007/978-3-642-20511-8_16 · Cited by 13 · see citing works
Using the bottom clause and mode declarations in FOL theory revision from examples
Duboc, A. L., Paes, A., & Zaverucha, G. (2009) · Machine Learning · doi:10.1007/s10994-009-5116-8 · Cited by 12 · see citing works
Deriving non-Zeno behaviour models from goal models using ILP
Alrajeh, D., Kramer, J., Russo, A., et al. (2009) · Formal Aspects of Computing · doi:10.1007/s00165-009-0128-5 · Cited by 11 · see citing works
A Refinement Operator for Theories
Badea, L. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44797-0_1 · Cited by 10 · see citing works
Nonmonotonic Learning in Large Biological Networks
Bragaglia, S., & Ray, O. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23708-4_3 · Cited by 10 · see citing works
Subsumer: A Prolog theta-subsumption engine
Santos, J. C. A., & Muggleton, S. (2010) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.iclp.2010.172 · Cited by 9 · see citing works
Using abduction for induction of normal logic programs
Ray, O., Flach, P. A., Kakas, A. C., et al. (2006) · Bristol Research (University of Bristol) · Cited by 9 · see citing works
Nonmonotomic Inductive Logic Programming
Sakama, C. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45402-0_5 · Cited by 6 · see citing works
An Inductive Approach for Modal Transition System Refinement
Alrajeh, D., Kramer, J., Russo, A., et al. (2011) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.iclp.2011.106 · Cited by 6 · see citing works
A backtracking strategy for order-independent incremental learning
Mauro, N. D., Esposito, F., Ferilli, S., et al. (2004) · Cited by 3 · see citing works
2nd International Workshop on the Induction of Process Models
Corapi, D., Ray, O., Russo, A., et al. (2008) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Biba, M., Basile, T. M. A., Ferilli, S., & Esposito, F. (2006). Improving scalability in ILP incremental systems. In Proceedings of CILC 2006-Italian conference on computational logic, Bari, Italy, pp. 26–27.
- Langley, P. (1995). Learning in humans and machines: Towards an interdisciplinary science, chapter order effects in incremental learning. Amsterdam: Elsevier.
- Luckham, D., & Schulte, R. (2008). Event processing glossary, version 1.1. Trento: Event Processing Technical Society.
Cited by
Sorted by citations
Online event recognition from moving vessel trajectories
Patroumpas, K., Alevizos, E., Artikis, A., et al. (2016) · GeoInformatica · doi:10.1007/s10707-016-0266-x · Cited by 119 · see citing works
Self-citationMitra, A., & Baral, C. R. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10354 · 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
“Other notable applications include learning event recognition systems (Katzouris et al., 2015, 2016), tracking the evolution of online communities (Athanasopoulos et al., 2018),”
BackgroundInfluentialAn Embedding-Based Approach to Rule Learning in Knowledge Graphs
Omran, P. G., Wang, K., & Wang, Z. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2941685 · Cited by 69 · see citing works
Inductive logic programming at 30
Cropper, A., Dumančić, S., Evans, R., & Muggleton, S. H. (2022) · Machine Learning · doi:10.1007/s10994-021-06089-1 · Cited by 61 · see citing works
Composite Event Recognition for Maritime Monitoring
Pitsikalis, M., Artikis, A., Dréo, R., et al. (2019) · ACM International Conference on Distributed and Event-Based Systems (DEBS) · doi:10.1145/3328905.3329762 · Cited by 49 · see citing works
Self-citationThe 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
Online learning of event definitions
KATZOURIS, N., ARTIKIS, A., & PALIOURAS, G. (2016) · Theory and Practice of Logic Programming · doi:10.1017/s1471068416000260 · Cited by 34 · see citing works
Self-citationWhat does the mind learn? A comparison of human and machine learning representations
Spicer, J., & Sanborn, A. N. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.004 · Cited by 28 · see citing works
Exception-Enriched Rule Learning from Knowledge Graphs
Gad-Elrab, M. H., Stepanova, D., Jacopo Urbani, A., & Weikum, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46523-4_15 · Cited by 25 · see citing works
Learning Through Hypothesis Refinement Using Answer Set Programming
Athakravi, D., Corapi, D., Broda, K., & Russo, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-44923-3_3 · Cited by 25 · see citing works
Rules, L. O.-R., & A, F. A. L. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4 · Cited by 24 · 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
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
Evans, R., Hernández-Orallo, J., Welbl, J., Kohli, P., & Sergot, M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2020.103438 · Cited by 58 · see citing works
Rule Induction and Reasoning over Knowledge Graphs
Stepanova, D., Gad-Elrab, M. H., & Ho, V. T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00338-8_6 · Cited by 20 · 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
Improving scalability of inductive logic programming via pruning and best-effort optimisation
Kazmi, M., Schüller, P., & Saygın, Y. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.06.013 · Cited by 17 · see citing works
Inductive learning of answer set programs for autonomous surgical task planning
Meli, D., Sridharan, M., & Fiorini, P. (2021) · Machine Learning · doi:10.1007/s10994-021-06013-7 · Cited by 14 · see citing works
Learning any memory-less discrete semantics for dynamical systems represented by logic programs
Ribeiro, T., Folschette, M., Magnin, M., et al. (2021) · Machine Learning · doi:10.1007/s10994-021-06105-4 · Cited by 14 · 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
Self-citationOnline 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
Self-citationSchüller, P., & Benz, M. (2018) · Machine Learning · doi:10.1007/s10994-018-5708-2 · Cited by 21 · see citing works
Towards Ethical Machines Via Logic Programming
Dyoub, A., Costantini, S., & Lisi, F. A. (2019) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.306.39 · Cited by 11 · see citing works
Meli, D., Fiorini, P., & Sridharan, M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.08.046 · Cited by 11 · see citing works
How not to drown in a sea of information: An event recognition approach
Alevizos, E., Artikis, A., Patroumpas, K., et al. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363849 · Cited by 9 · see citing works
Self-citationIterative 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
Self-citationLogic Programming and Machine Ethics
Dyoub, A., Costantini, S., & Lisi, F. A. (2020) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.325.6 · Cited by 8 · see citing works
Towards an ILP Application in Machine Ethics
Dyoub, A., Costantini, S., & Lisi, F. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49210-6_3 · 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
Parallel Online Learning of Event Definitions
Katzouris, N., Artikis, A., & Παλιούρας, Γ. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78090-0_6 · Cited by 7 · see citing works
Self-citationSituation Mining: Event Pattern Mining for Situation Model Induction
Salfinger, A. (2019) · IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) · doi:10.1109/cogsima.2019.8724300 · Cited by 6 · see citing works
Interactive Extreme-Scale Analytics: Towards Battling Cancer
Giatrakos, N., Ponce-de-León, M., Tartaglia, G. G., Valencia, A., Zissis, D., Katzouris, N., Deligiannakis, A., Artikis, A., Garofalakis, M., Paliouras, G., et al. (2019) · IEEE Technology and Society Magazine · doi:10.1109/mts.2019.2913071 · Cited by 7 · see citing works
Self-citationSpecifying and Executing Open Multi-agent Systems
Artikis, A., Sergot, M. J., Pitt, J. V., et al. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-33570-4_10 · Cited by 5 · see citing works
Self-citationSearch Space Expansion for Efficient Incremental Inductive Logic Programming from Streamed Data
Law, M., Broda, K. B., & Russo, A. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/374 · Cited by 5 · see citing works
The Complex Event Recognition Group
Alevizos, E., Artikis, A., Katzouris, N., et al. (2018) · ACM SIGMOD Record · doi:10.1145/3299887.3299899 · Cited by 4 · see citing works
Self-citationData-Driven Revision of Conditional Norms in Multi-Agent Systems
Dell’Anna, D., Alechina, N., Dalpiaz, F., et al. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13683 · Cited by 4 · see citing works
Reactive Reasoning with the Event CalculusPreprint
Artikis, A., Sergot, M. J., & Παλιούρας, Γ. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05364 · Cited by 4 · see citing works
Self-citationComposite Event Recognition for Maritime MonitoringPreprint
Pitsikalis, M., Artikis, A., Dréo, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03078 · Cited by 4 · see citing works
Self-citationSelf-evolvable knowledge-enhanced IoT data mobility for smart environment
Xiao, B. (2017) · doi:10.1145/3109761.3109789 · Cited by 3 · see citing works
Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback
Wu, Y. X., Chen, J., Haxhidauti, P., et al. (2020) · EPiC series in computing · doi:10.29007/ppgx · Cited by 3 · see citing works
Systematic review on event prediction models
Jamaludin, F. S., & Ramasamy, R. K. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v19.i3.pp1490-1496 · Cited by 3 · see citing works
Zaverucha, G., Costa, V. S., & Paes, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-44923-3 · Cited by 745 · see citing works
Evaluating the Apperception EnginePreprint
Evans, R., Hernández‐Orallo, J., Welbl, J., Kohli, P., & Sergot, M. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05367 · Cited by 3 · see citing works
Learning Commonsense Knowledge through Interactive DialoguePreprint
Wu, B., Russo, A., Law, M., & Inoue, K. (2018) · EasyChair preprint · doi:10.29007/lrph · Cited by 3 · see citing works
Event Recognition Under Uncertainty and Incomplete Data
Skarlatidis, A., & Σκαρλατίδης, Α. (2014) · Cited by 3 · see citing works
Logic-Based Online Complex Event Rule Learning with Weight Optimization
Wang, Y., Zhu, R., Wang, Z., et al. (2019) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2019.00030 · Cited by 2 · see citing works
Graph Structures for Knowledge Representation and Reasoning
Cochez, M., Madalina Croitoru, A., Marquis, P., & Rudolph, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72308-8 · Cited by 2 · see citing works
Question Answering with LLMs and Learning from Answer Sets
Borroto, M., GALLAGHER, K., Ielo, A., KAREEM, I., Ricca, F., & Russo, A. (2025) · Theory and Practice of Logic Programming · doi:10.1017/s1471068425100343 · Cited by 1 · see citing works
Learning explanations for biological feedback with delays using an event calculus
Srinivasan, A., Bain, M., & Baskar, A. (2021) · Machine Learning · doi:10.1007/s10994-021-06038-y · Cited by 1 · see citing works
Revising event calculus theories to recover from unexpected observations
Tsampanaki, N., Patkos, T., Flouris, G., et al. (2019) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-019-09663-5 · Cited by 1 · see citing works
Inferring Logical Clauses for Answering Complex Multi-hop Open Domain Questions
Galitsky, B. A. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-52167-7_7 · Cited by 1 · see citing works
The Complexity of Norm Synthesis and Revision
Dell’Anna, D., Alechina, N., Dalpiaz, F., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20845-4_3 · Cited by 1 · see citing works
Galindo, M. J. O., & Moreno, L. A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72308-8_4
Induction Learning Based on Specific Objects
Wang, H., Gui, T., & Niu, Q. (2021) · doi:10.1145/3480571.3480584
Learning and Revising Dynamic Temporal Theories in the Full Discrete Event Calculus
Ray, O. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97454-1_16
Diagnosis of Event Sequences with LFIT
Ribeiro, T., Folschette, M., Magnin, M., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55630-2_9
Making Sense of Raw Input (Extended Abstract)
Evans, R., Bošnjak, M., Buesing, L., et al. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/799
Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts
Wang, X., Li, Q., & Bao, X. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.047586
An Incremental Learning Algorithm For Non-deterministic Interpretation Transitions
Huang, Y., Jiang, N., Zhou, A., et al. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450984
- Authors
- Katzouris, N., Artikis, A., & Paliouras, G.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- University of Piraeus (GR)
- Corresponding author
- Katzouris, Nikos
- Artikis, Alexander
- Venue
- Machine Learning
- Published
- 2015-06-20
- Volume
- 100
- Issue
- 2-3
- Pages
- 555-585
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1500684598
- Semantic Scholar
- c254b8e03b7828d5eeee9179f6232f968478d76b
- Cited by
- 62
- References
- 78
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 7 · 2021: 9 · 2020: 9 · 2019: 11 · 2018: 7 · 2017: 2 · 2016: 7 · 2015: 2 · 2014: 3
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (619435)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- incremental learning, event recognition, event definition, inductive logic programming, event calculus, temporal knowledge bases, activity recognition, urban transport
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
