The DOI matches its Crossref record; 6 of the 7 references it cites were found in a registry or an index.
Explanation-Based Generalization as Resolution Theorem Proving
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“A key difference is that inductive generalisation and predicate invention are not achieved in existing EBG paradigms, which assume a complete first-order domain theory.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Kedar-Cabelli and McCarty 1987
Citations
Cites
Foundations of Logic Programming
Lloyd, J. (1984) · Symbolic Computation · doi:10.1007/978-3-642-96826-6 · Cited by 5,772
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
Self-citationExplanation-Based Learning: An Alternative View
DEJONG, G. F., & Mooney, R. J. (1986) · Machine Learning · doi:10.1023/a:1022898111663 · Cited by 839
The Art of Prolog: Advanced Programming Techniques
STERLING, L. S., & Shapiro, E. Y. (1994) · Cited by 532
A domain independent explanation-based generalizer
Mooney, R. J., & Bennett, S. (1986) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 109
Explanation-based generalization in a logic-programming environment
Hirsh, H. (1987) · International Joint Conference on Artificial Intelligence · Cited by 60
Research works printed but not linked: 3
- Kedar-Cabelli, S. T. (1987). Investigations into purpose-directed analogy. Doctoral dissertation, Department of Computer Science, Rutgers University, New Brunswick, NJ, (in progress).
- Minton, S. (1987). Acquiring search control knowledge: An explanation-based approach. Doctoral dissertation, Computer Science Department, Carnegie-Mellon University, Pittsburgh, PA, (in progress).
- Sims, M. (June 1987). Analytic and Empirical Discovery in IL. Proceedings of the Fourth International Machine Learning Workshop. University of California at Irvine, Morgan Kaufmann.
Cited by
Sorted by citations
Explanation-based learning:A problem solving perspective
Minton, S., Carbonell, J., Knoblock, C. A., Kuokka, D. R., Etzioni, O., & Gil, Y. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90047-7 · Cited by 228
Computational approaches to analogical reasoning
Hall, R. P. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90003-9 · Cited by 223
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
Machine Invention of First-order Predicates by Inverting Resolution
MUGGLETON, S., & BUNTINE, W. (1988) · Machine Learning Proceedings 1988 · doi:10.1016/b978-0-934613-64-4.50040-2 · Cited by 436
A Study of Explanation-Based Methods for Inductive Learning
Flann, N. S., & Dietterich, T. G. (1989) · Machine Learning · doi:10.1023/a:1022652016863 · Cited by 147
“For example, Laird [1986] has found that the encoding of the eight-puzzle problem space in SOAR critically influences the quality and generality of the chunks that are learned.”
Background
Metadata
- Authors
- KEDAR-CABELLI, S. T., & MCCARTY, L. T.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- KEDAR-CABELLI, SMADAR T.
- MCCARTY, L. THORNE
- Venue
- Proceedings of the Fourth International Workshop on MACHINE LEARNING
- Publisher
- Elsevier
- Published
- 1987
- Pages
- 383-389
- OpenAlex
- W1581847324
- Cited by
- 123
- References
- 7
- Citations by year
- 2019: 1 · 2018: 2 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 14.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Elsevier eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Software › Logic, programming, and type systems
- Keywords
- explanation-based generalization, resolution theorem proving, Horn clause logic, inductive generalization
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
