Inductive Logic Programming as Abductive Search
Open accessAbstract
We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.
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.
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?
ASPAL builds on TAL.
“ASPAL builds on TAL (Corapi et al., 2010),”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Corapi et al., 2010
The Introduction and Section 5 describe TAL and its implementation, but do not mention ASPAL or say that it builds on TAL. Searches for “ASPAL” and for a successor relationship found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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
Reality mining: sensing complex social systems
Eagle, N., & (Sandy) Pentland, A. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-005-0046-3 · Cited by 2,772 · see citing works
Clark, K. (1978) · Springer eBooks · doi:10.1007/978-1-4684-3384-5_11 · Cited by 2,111 · see citing works
Foundations of logic programming; (2nd extended ed.)
Lloyd, J. W. (1987) · Springer eBooks · Cited by 1,501 · see citing works
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411 · see citing works
Sterling, L., Shapiro, E., & Garrett, R. (1987) · IEEE Expert · doi:10.1109/mex.1987.4307074 · Cited by 1,101 · 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
The Art of Prolog: Advanced Programming Techniques
STERLING, L. S., & Shapiro, E. Y. (1994) · Cited by 532 · see citing works
Shanahan, M. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48317-9_17 · Cited by 444 · 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
Learning First-Order Definitions of Functions
Quinlan, J. R. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.308 · Cited by 89 · see citing works
Cohen, W. W. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90070-1 · Cited by 84 · 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
Learning 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
Self-citationA-system: problem solving through abduction
Kakas, A. C., Nuffelen, B. V., & Denecker, M. (2001) · Lirias · Cited by 75 · see citing works
Which hypotheses can be found with inverse entailment?
Yamamoto, A. (1997) · Lecture notes in computer science · doi:10.1007/3540635149_58 · Cited by 55 · see citing works
Kakas, A. C., & Riguzzi, F. (2000) · New Generation Computing · doi:10.1007/bf03037531 · 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
Mueller, E. T. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-801416-5.00002-4 · Cited by 39 · 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
Learning Recursive Theories with ATRE.
Malerba, D., Esposito, F., & Lisi, F. A. (1998) · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · Cited by 26 · 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
Self-citationAILP abductive inductive logic programming
Adé, H., & Denecker, M. (1995) · Cited by 20 · see citing works
Relational Data Mining Applications: An Overview
Džeroski, S. (2001) · Springer eBooks · doi:10.1007/978-3-662-04599-2_14 · 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
Self-citationAn 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
A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks
Ray, O., Whelan, K. E., & King, R. D. (2009) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2009.175 · Cited by 11 · see citing works
Inferring the Function of Genes from Synthetic Lethal Mutations
Ray, O., & Bryant, C. H. (2008) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2008.124 · Cited by 9 · see citing works
Inferring process models from temporal data with abduction and induction
Ray, O. (2007) · Bristol Research (University of Bristol) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 31 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- H. Boström and P. Idestam-Almquist. Specialization of logic programs by pruning SLD-trees. GMD-Studien, vol. 237. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994.
- John W. Lloyd. Foundations of Logic Programming, 2nd Edition. Springer, 1987.
- Stefan Wrobel. First order theory refinement. In Luc De Raedt (ed.), Advances in Inductive Logic Programming, pp. 14 -33. IOS Press, 1996.
Cited by
Sorted by citations
Learning Explanatory Rules from Noisy Data
Evans, R., & Grefenstette, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5714 · Cited by 426 · 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
Self-citationILP 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
Logic-based technologies for multi-agent systems: a systematic literature review
Calegari, R., Ciatto, G., Mascardi, V., & Omicini, A. (2021) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-020-09478-3 · Cited by 117 · see citing works
“R [159], tuProlog [122], Xsb [309], YAP Prolog [93] No logic AGENTFLY [322], Aglets [221], CArtAgO [298], ConArg [38], cougaar [173], JaCaMo [41], JADE-JBossESB [346], JACKAL [92], Klaim [115], Magentix2 [328], MASON [238], Moise & S-Moise+ [193], Presage2 [71], RETSINA [329], SARL APL [302] Technology not found ABLE…”
BackgroundLearning 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
“…based on their semantics, such as whether they are functional, irreflexive, etc. Mode declarations (Muggleton 1995) are a popular language bias (Blockeel and De Raedt 1998; Srinivasan 2001; Ray 2009; Corapi et al. 2010, 2011; Athakravi et al. 2013; Ahlgren and Yuen 2013; Law et al. 2014).”
BackgroundInductive 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
“…the most popular setting (Muggleton, 1987; Muggleton & Buntine, 1988; Muggleton & Feng, 1990; Quinlan, 1990; Muggleton, 1995; Bratko, 1999; Srinivasan, 2001; Ray, 2009; Corapi et al., 2010, 2011; Athakravi et al., 2013; Cropper & Muggleton, 2016; Evans & Grefenstette, 2018; Cropper & Morel, 2020).”
BackgroundMethodInfluentialInductive 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
Self-citationIncremental 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
Learning revised models for planning in adaptive systems
Sykes, D., Corapi, D., Magee, J., Kramer, J., Russo, A., & Inoue, K. (2013) · doi:10.5555/2486788.2486797 · Cited by 45 · 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
Self-citationLearning revised models for planning in adaptive systems
Sykes, D., Corapi, D., Magee, J., Kramer, J., Russo, A., & Inoue, K. (2013) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2013.6606552 · Cited by 40 · see citing works
Self-citationElaborating Requirements Using Model Checking and Inductive Learning
Alrajeh, D., Kramer, J., Russo, A., & Uchitel, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.41 · Cited by 38 · see citing works
Self-citationGenerating obstacle conditions for requirements completeness
Alrajeh, D., Kramer, J., van Lamsweerde, A., Russo, A., & Uchitel, S. (2012) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2012.6227147 · Cited by 36 · see citing works
Self-citationGenerating Obstacle Conditions for Requirements Completeness
Alrajeh, D., Kramer, J., van Lamsweerde, A., Russo, R., & Uchitel, S. (2012) · Cited by 36 · 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
Self-citationCompleting causal networks by meta-level abduction
Inoue, K., Doncescu, A., & Nabeshima, H. (2013) · Machine Learning · doi:10.1007/s10994-013-5341-z · Cited by 33 · 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
Self-citationLearning Explanatory Rules from Noisy Data (Extended Abstract)
Evans, R., & Grefenstette, E. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/792 · Cited by 27 · 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
Self-citationPredicate 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
Self-citationRepresenting and Learning Grammars in Answer Set Programming
Law, M., Russo, A., Bertino, E., Broda, K. B., & Lobo, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012919 · Cited by 22 · see citing works
Self-citationThe 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
Self-citationProbabilistic Rule Learning in Nonmonotonic Domains
Corapi, D., Sykes, D., Inoue, K., & Russo, A. (2011) · Computational Logic in Multi-Agent Systems · doi:10.1007/978-3-642-22359-4_17 · Cited by 24 · see citing works
Self-citationTowards Nonmonotonic Relational Learning from Knowledge Graphs
Tran, H. D., Stepanova, D., Gad-Elrab, M. H., Lisi, F. A., & Weikum, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63342-8_8 · Cited by 21 · see citing works
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
Self-citationRule 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
Automated support for diagnosis and repair
Alrajeh, D., Kramer, J., Russo, A., & Uchitel, S. (2015) · Communications of the ACM · doi:10.1145/2658986 · Cited by 18 · see citing works
Self-citationKeystroke Dynamic Authentication in Mobile Cloud Computing
Babaeizadeh, M., Bakhtiari, M., & Maarof, M. A. (2014) · International Journal of Computer Applications · doi:10.5120/15541-4274 · Cited by 15 · 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
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
Sen, P., Danilevsky, M., Li, Y., Brahma, S., Böehm, M., Chiticariu, L., & Krishnamurthy, R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.345 · Cited by 12 · see citing works
Abduction and Argumentation for Explainable Machine Learning: A Position SurveyPreprint
Kakas, A. C., & Michael, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12896 · 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
Self-citationScalable Non-observational Predicate Learning in ASP
Law, M., Russo, A., Broda, K. B., & Bertino, E. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/267 · Cited by 9 · see citing works
Self-citationRisk-driven Engineering of Requirements for Dependable Systems
van Lamsweerde, A. (2013) · NATO science for peace and security series. D, Information and communication security · doi:10.3233/978-1-61499-207-3-207 · Cited by 9 · see citing works
Learning User Behaviours in Real Mobile Domains
Markitanis, A., Corapi, D., Russo, A., & Lupu, E. (2014) · IMPERIAL COLLEGE PRESS eBooks · doi:10.1142/9781783265091_0006 · Cited by 8 · see citing works
Self-citationLearning Answer Set Programming Rules For Ethical Machines.
Dyoub, A., Costantini, S., & Lisi, F. A. (2019) · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · Cited by 8 · see citing works
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
Cunnington, D., Law, M., Lobo, J., & Russo, A. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/399 · Cited by 8 · see citing works
Self-citationA Kantian Cognitive Architecture
Evans, R. (2019) · Philosophical studies series · doi:10.1007/978-3-030-01800-9_13 · Cited by 8 · see citing works
Cumulative Learning in the Lambda Calculus
Henderson, R. (2013) · doi:10.25560/24759 · Cited by 8 · see citing works
Inductive Learning Using Constraint-Driven Bias
Athakravi, D., Alrajeh, D., Broda, K. B., Russo, A., & Satoh, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23708-4_2 · Cited by 7 · see citing works
Self-citationTop 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
Automated Error-Detection and Repair for Compositional Software Specifications
Alrajeh, D., & Craven, R. J. B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10431-7_9 · Cited by 6 · see citing works
Norm Refinement and Design through Inductive Learning
Corapi, D., Vos, M. D., Padget, J. A., Russo, A., & Satoh, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21268-0_5 · Cited by 5 · see citing works
Self-citationDoes Multi-Clause Learning Help in Real-World Applications?
Lin, D., Chen, J., Watanabe, H., Muggleton, S., Jain, P., Sternberg, M. J. E., Baxter, C., Currie, R. A., Dunbar, S. J., Earll, M. E., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31951-8_21 · Cited by 4 · see citing works
Logic-Based Learning: Theory and Application
Alrajeh, D., & Russo, A. (2018) · Spiral (Imperial College London) · doi:10.1007/978-3-319-96562-8_9 · Cited by 4 · see citing works
Self-citationOn Minimality and Integrity Constraints in Probabilistic Abduction
Turliuc, C.-R., Maimari, N., Russo, A., & Broda, K. B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45221-5_51 · Cited by 4 · see citing works
Self-citationRevising Institutions Governed by Institutions for Compliant Regulations
King, T. C., Li, T., Vos, M. D., Jonker, C. M., Padget, J. A., & van Riemsdijk, M. B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42691-4_11 · 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
Towards learning to detect meaningful changes in software
Yu, Y., Bandara, A. K., Tun, T. T., & Nuseibeh, B. (2011) · doi:10.1145/2070821.2070828 · Cited by 2 · see citing works
Biography and future challenges
Muggleton, S., Raedt, L. D., Poole, D., Ivan Bratko, A., Flach, P. A., Inoue, K., & Srinivasan, A. (2011) · Cited by 1 · see citing works
Computational alignment of goals and scenarios for complex systems
Alrajeh, D., Russo, A., Lockerbie, J., Maiden, N., Mavin, A., & Novak, M. (2013) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2013.6606690 · Cited by 1 · see citing works
Self-citationA Data Protection Architecture for Derived Data Control in Partially Disconnected Networks
Scalavino, E. (2011) · Spiral (Imperial College London) · doi:10.25560/10203
Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs.
Lisi, F. A., & Stepanova, D. (2017) · MPG.PuRe (Max Planck Society)
THE ROLE OF INDUCTIVE LOGIC PROGRAMMING IN SOME MACHINE LEARNING APPLICATIONS
Awwad, M. (2018) · Humanities Studies · doi:10.17721/2521-6805.2018/31-1/11
- Authors
- Corapi, D., Russo, A., & Lupu, E.
- Affiliations
- Imperial College London (GB)
- Venue
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Publisher
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Published
- 2010
- OpenAlex
- W2150097634
- Cited by
- 60
- References
- 31
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 7 · 2017: 2 · 2016: 3 · 2015: 4 · 2014: 6 · 2013: 8 · 2012: 6
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- green
- drops.dagstuhl.de
- Licence
- Locations
- DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (submittedVersion, cc-by-nc-nd)
- Leibniz international proceedings in informatics (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/F023294/1)
- 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 › AI-based Problem Solving and Planning
- Keywords
- inductive logic programming, abductive logic programming, nonmonotonic reasoning, top-down search, background theory, normal logic programs, integrity constraints, language bias, Tal
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
