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Explanation-Based Generalization as Resolution Theorem Proving
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“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.
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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.”
BackgroundExplanation-based learning: a survey of programs and perspectives
Ellman, T. (1989) · ACM Computing Surveys · doi:10.1145/66443.66445 · Cited by 138
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
Muggleton, S., Schmid, U., Zeller, C., Tamaddoni‐Nezhad, A., & Besold, T. R. (2018) · Machine Learning · doi:10.1007/s10994-018-5707-3 · Cited by 116
“For example, Prolog-EBG (Kedar-Cabelli and McCarty 1987) automatically formulates meaningful constraints required in the rules underlying the training examples.”
MethodLearning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System
Bergadano, F., Matwin, S., Michalski, R. S., & Zhang, J. (1992) · Machine Learning · doi:10.1023/a:1022682318197 · Cited by 103
Acquiring search-control knowledge via static analysis
Etzioni, O. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90080-u · Cited by 102
Explanation-based generalisation = partial evaluation
van Harmelen, F., & Bundy, A. (1988) · Artificial Intelligence · doi:10.1016/0004-3702(88)90088-4 · Cited by 102
“The main goal of PE is to perform as much of the computation in a program as possible without depending on any of the input values of the program.”
BackgroundAn Approach to Combining Explanation-based and Neural Learning Algorithms
SHAVLIK, J. W., & Towell, G. G. (1989) · Connection Science · doi:10.1080/09540098908915640 · Cited by 100
Learning-assisted automated planning: looking back, taking stock, going forward
Zimmerman, T. L., & Subbarao Kambhampati, B. (2003) · doi:10.1609/aimag.v24i2.1705 · Cited by 85
Weld, D. S. (1988) · Artificial Intelligence · doi:10.1016/0004-3702(88)90086-0 · Cited by 83
Artificial Intelligence for Design
Stahovich, T. F. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511529627.010 · Cited by 81
An implementation of Eisner v. Macomber
McCarty, L. T. (1995) · International Conference on Artificial Intelligence and Law · doi:10.1145/222092.222258 · Cited by 68
Self-citationAcquiring Recursive and Iterative Concepts with Explanation-Based Learning
SHAVLIK, J. W. (1990) · Machine Learning · doi:10.1023/a:1022659708512 · Cited by 63
“Standard explanation-based learning algorithms (e.g., Fikes, Hart, & Nilsson, 1972; Hirsh, 1987; Kedar-Cabelli & McCarty, 1987; Mooney & Bennett, 1986) and similar algorithms for chunking (Laird, Rosenbloom, & Newell, 1986) cannot treat these cases differently.”
Failsafe: a floor planner that uses EBG to learn from its failures
Mostow, J., & Bhatnagar, N. (1987) · International Joint Conference on Artificial Intelligence · Cited by 63
PROLEARN: towards a prolog interpreter that learns
Prieditis, A. E., & Mostow, J. (1987) · National Conference on Artificial Intelligence · Cited by 51
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning
Cohen, W. W. (1988) · Elsevier eBooks · doi:10.1016/b978-0-934613-64-4.50032-3 · Cited by 49
“Typically this limitation shows up as an inability 1By \standard EBL techniques" I mean published domain-independent explanation-based learning algorithms, such as EGGS [15], PROLOG EBG [8], MRS-EBG [6], and PROLEARN [16].”
The effect of rule use on the utility of explanation-based learning
Mooney, R. J. (1989) · International Joint Conference on Artificial Intelligence · Cited by 46
Learning two-tiered descriptions of flexible concepts: The POSEIDON system
Bergadano, F., Matwin, S., Michalski, R. S., & Zhang, J. (1992) · Machine Learning · doi:10.1007/bf00994004 · Cited by 45
Samuelsson, C., & Rayner, M. (1991) · Cited by 42
Theories of Comparative Analysis
Weld, D. S. (1990) · Cited by 37
Acquiring recursive and iterative concepts with explanation-based learning
Shavlik, I. W. (1990) · Machine Learning · doi:10.1007/bf00115894 · Cited by 36
Explicitly biased generalization
Gordon, D. F., & Perlis, D. R. (1989) · Computational Intelligence · doi:10.1111/j.1467-8640.1989.tb00317.x · Cited by 35
Introduction to machine learning
Kubát, M. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55681-8_33 · Cited by 33
“A good introduction can be found in [21] and in [6, 2]. A detailed description of the algorithm, written in Prolog, is discussed in [ 12 ].”
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
Reasoning about Operationality for Explanation-Based Learning
Hirsh, H. (1988) · Elsevier eBooks · doi:10.1016/b978-0-934613-64-4.50028-1 · Cited by 23
Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches
Mitchell, T. M., & Thrun, S. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50032-0 · Cited by 20
“Both Prolog-EBG and EBNN construct similar explanations: chains of inferences based on the domain theory , predicting the observed target function value from attributes of the examples.”
InfluentialFailure recovery planning in assembly based on acquired experience: learning by analogy
Lopes, L. S. (2003) · IEEE International Symposium on Assembly and Task Planning (ISATP) · doi:10.1109/isatp.1999.782974 · Cited by 19
“The specific generalisation method that is applied finds its roots in explanation-based learning (EBL) [3,7] and goes through the following steps: • Variabilisation: replace each occurrence of a constant by a different variable.”
MethodInfluentialProgramming by example and proving by example using higher-order unification
Hagiya, M. (1990) · Lecture notes in computer science · doi:10.1007/3-540-52885-7_116 · Cited by 19
Kawakami, H., Katai, O., Sawaragi, T., Konishi, T., & Iwai, S. (1996) · Artificial Intelligence in Engineering · doi:10.1016/0954-1810(95)00028-3 · Cited by 19
An apprentice-based approach to knowledge acquisition
Mahadevan, S., Mitchell, T. M., Mostow, J., Steinberg, L. I., & Tadepalli, P. V. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90059-k · Cited by 19
ABDUCTION and EXPLANATION‐BASED LEARNING: CASE STUDIES IN DIVERSE DOMAINS
O’Rorke, P. (1994) · Computational Intelligence · doi:10.1111/j.1467-8640.1994.tb00167.x · Cited by 18
“8 Kedar-Cabelli & McCarty, 1987; Prieditis & Mostow, 1987) will fail to explain, generalize, and learn from this example because there is no proof of chills and pain from the known facts.”
BackgroundMethodInfluentialDEJONG, G. F. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429171529-37 · Cited by 17
Experience learning in model-based diagnostic systems
Koseki, Y. (1989) · International Joint Conference on Artificial Intelligence · Cited by 17
Explanation based program transformation
Bruynooghe, M., Raedt, L. D., & Schreye, D. D. (1989) · Lirias · Cited by 16
Walther, C., & Kolbe, T. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00096-x · Cited by 15
AUGMENTING DOMAIN THEORY FOR EXPLANATION-BASED GENERALISATION
Ali, K. (1989) · Elsevier eBooks · doi:10.1016/b978-1-55860-036-2.50016-3 · Cited by 13
An Analysis of Representation Shift In Concept Learning
Cohen, W. W. (1990) · Elsevier eBooks · doi:10.1016/b978-1-55860-141-3.50016-x · Cited by 12
Searching for Operational Concept Descriptions in BAR, MetaLEX, and EBG
Mostow, J. (1987) · Elsevier eBooks · doi:10.1016/b978-0-934613-41-5.50040-4 · Cited by 12
Learning and applying generalised solutions using higher order resolution
Donat, M. R., & Wallen, L. A. (2005) · Springer eBooks · doi:10.1007/bfb0012822 · Cited by 12
“The work of Kedar-Cabelli and McCarty [ 4 ] is a notable exception.”
BackgroundMethodInfluentialHigher-Order and Modal Logic as a Framework for Explanation-Based Generalization
Dietzen, S., & Pfenning, F. (1992) · Machine Learning · doi:10.1023/a:1022676712939 · Cited by 12
“The above example, as well as those to follow, is formulated in λProlog.”
BackgroundMethodHow to improve the reliability of expert systems
Bundy, A. (1988) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 12
Learning Relations by Path Finding and Simultaneous Covering
Gao, Z., Zhang, Z., & Huang, Z. (2009) · doi:10.1109/csie.2009.100 · Cited by 11
LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica
O’Rorke, P. (1989) · Machine Learning · doi:10.1023/a:1022647915955 · Cited by 11
“In contrast to EBG macrolearning systems based on PROLOG [6, 19] and MRS [5] only generalized conclusions are learned in the EBL LT.”
MethodEliminating Redundancy in Explanation-Based Learning
Boström, H. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50010-3 · Cited by 11
What inductive bias gives good neural network training performance?
Snyders, S., & Omlin, C. W. P. (2000) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2000.861348 · Cited by 11
Learning search control knowledge: An explanation-based approach
Zito-Wolf, R. J. (1991) · Machine Learning · doi:10.1007/bf00114164 · Cited by 10
“This equivalence has been made precise (Kedar-Cabelli & McCarty, 1987; van Harmelan & Bundy, 1988), a result that is not adequately appreciated in the literature.”
The What, the Why, and the How of Artificial Explanations in Automated Decision-MakingPreprint
Besold, T. R., & Uckelman, S. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07074 · Cited by 23
“The deductive proof of an example—in some cases augmented by newly added features not explicit in the training examples (Kedar-Cabelli & McCarty, 1987)—yields a specialization of the given domain theory leading to the generation of a special-purpose sub-theory described in a user-defined operational…”
MethodMC-TopLog: Complete Multi-clause Learning Guided by a Top Theory
Muggleton, S., Lin, D., & Tamaddoni‐Nezhad, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31951-8_22 · Cited by 9
“Specifically, EBG derives its generalisations from background knowledge, while (cid:2) DTD’s generalisations are derived from a top theory, which can compose hypothesis language that do not exist in the background knowledge.”
BackgroundInfluentialFAILURE RECOVERY PLANNING FOR ROBOTIZED ASSEMBLY BASED ON LEARNED SEMANTIC STRUCTURES
Lopes, L. S. (2007) · IFAC Proceedings Volumes · doi:10.3182/20070523-3-es-4907.00011 · Cited by 9
Learning-assisted automated planning
ZimmermanTerry, & KambhampatiSubbarao (2003) · AI Magazine · Cited by 9
A Tool for the Management of Incomplete Theories: Reasoning about Explanation
Duval, B., & Kodratoff, Y. (1990) · Kluwer international series in engineering and computer science · doi:10.1007/978-1-4613-1641-1_7 · Cited by 9
Omlin, C. W. P., & Snyders, S. (2003) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00062-9 · Cited by 8
Ploetzner, R., & Fehse, E. (1998) · HAL · Cited by 7
Mooney, R. J., & Zelle, J. M. (1994) · ACM SIGART Bulletin · doi:10.1145/181668.181673 · Cited by 6
“Several algorithms have been developed for correctly performing the generalization step [28, 17].”
MethodOne-shot Information Extraction from Document Images using Neuro-Deductive Program SynthesisPreprint
Sunder, V., Srinivasan, A., Vig, L., Shroff, G. M., & Rahul, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02427 · Cited by 12
“Deductive program synthesis can be seen as a form of ExplanationBased Generalization (EBG) [10].”
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
Dietzen, S., & Pfenning, F. (1989) · Elsevier eBooks · doi:10.1016/b978-1-55860-036-2.50113-2 · Cited by 7
Towards intelligent execution supervision for flexible assembly systems
Lopes, L. S., & Camarinha-Matos, L. M. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1996.571283 · Cited by 6
Katai, O., Sawaragi, T., Iwai, S., & Kawakami, H. (1991) · Elsevier eBooks · doi:10.1016/b978-0-7506-1188-6.50019-3 · Cited by 6
Extending EBG to term-rewriting systems
Laird, P. D., & Gamble, E. (1990) · National Conference on Artificial Intelligence · Cited by 6
Explanation-based learning: its role in problem solving
Krawchuk, B. J., & Witten, I. H. (1989) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528138908953692 · Cited by 5
Learning failure recovery knowledge for mechanical assembly
Lopes, L. S., & Camarinha-Matos, L. M. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1996.571041 · Cited by 5
Explanation-based interpretation of open-textured concepts in logical models of legislation
Costantini, S., & Lanzarone, G. A. (1995) · Artificial Intelligence and Law · doi:10.1007/bf00872530 · Cited by 5
“In Logic Programming, EBG was introduced by the works reported in Prieditis & Mostow [1987], Hirsch [1987], Kedar-Cabelli & McCarty [1987] , and, since then, this kind of formalization has also been used to describe most of the previous domain-independent characterizations of EBG.”
MethodExplanation-Based Learning: A survey
Wusteman, J. (1992) · Artificial Intelligence Review · doi:10.1007/bf00155763 · Cited by 5
“They are epitomised by the concept of the "micro-world" which is described by Haugeland ( Haugeland 1985 ) as”
BackgroundAbduction for explanation-based learning
Duval, B. (1991) · Lecture notes in computer science · doi:10.1007/bfb0017029 · Cited by 5
Learning by explanation of failures
Urbano, P. (1991) · Lecture notes in computer science · doi:10.1007/bfb0017024 · Cited by 5
AN EMPIRICAL ANALYSIS OF EBL APPROACHES FOR LEARNING PLAN SCHEMATA
SHAVLIK, J. W. (1989) · Elsevier eBooks · doi:10.1016/b978-1-55860-036-2.50052-7 · Cited by 5
ACQUIRING GENERAL ITERATIVE CONCEPTS BY REFORMULATING EXPLANATIONS OF OBSERVED EXAMPLES
SHAVLIK, J. W., & DEJONG, G. F. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051055-2.50018-3 · Cited by 5
LEARNING EXPERT KNOWLEDGE BY IMPROVING THE EXPLANATIONS PROVIDED BY THE SYSTEM
Kodratoff, Y. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051055-2.50024-9 · Cited by 5
Bruynooghe, M., & Schreye, D. D. (1989) · Lecture notes in computer science · doi:10.1007/3-540-51734-0_52 · Cited by 4
Proving inductive properties of PROLOG programs in second-order intuitionistic logic
McCarty, L. T. (1993) · International Conference on Logic Programming · Cited by 4
Self-citationHigher-order and modal logic as a framework for explanation-based generalization
Dietzen, S., & Pfenning, F. (1992) · Machine Learning · doi:10.1007/bf00993253 · Cited by 4
Learning apprentice system for turbine modelling
Sawar, M. J., & Thomas, R. C. (1990) · Industrial and Engineering Applications of Artificial Intelligence and Expert Systems · doi:10.1145/98894.99106 · Cited by 3
Explanation-Based Learning as Concept Formation
Mooney, R. J. (1991) · Elsevier eBooks · doi:10.1016/b978-1-4832-0773-5.50013-4 · Cited by 3
Creating high level knowledge structures from simple elements
Pazzani, M. J. (2005) · Springer eBooks · doi:10.1007/bfb0017227 · Cited by 3
Improving example-guided unfolding
Boström, H. (1993) · Lecture notes in computer science · doi:10.1007/3-540-56602-3_132 · Cited by 3
“The benefits of adding clauses learned by explanation-based generalization (EBG) [13, 9] to a logic program come from reordering effects and decreased path costs when the clauses are successfully applied (cf.”
Explaining and Generalizing Diagnostic Decisions
O’Rorke, P. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50036-8 · Cited by 3
An augmented EBL and its application to the utility problem
Yamamura, M., & Kobayashi, S. (1991) · Cited by 3
Katai, O., Kawakami, H., Sawaragi, T., Iwai, S., & KONISHI, T. (1990) · Transactions of the Society of Instrument and Control Engineers · doi:10.9746/sicetr1965.26.916 · Cited by 3
A Perspective on Explanation—Based Learning
DEJONG, G. F. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3602-4_1 · Cited by 3
Explanation-based learning helps acquire knowledge from natural language texts
Delisle, S., Matwin, S., Wang, J., & Zupan, L. H. (1991) · Lecture notes in computer science · doi:10.1007/3-540-54563-8_96 · Cited by 2
CONDITIONAL OPERATIONALITY AND EXPLANATION-BASED GENERALIZATION
Hirsh, H. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051055-2.50021-3 · Cited by 2
EBG — ein deduktives Lernverfahren
Hecht, A. (1989) · Informatik-Fachberichte · doi:10.1007/978-3-642-75182-0_9 · Cited by 2
Explanation Generalization in EGGS
Mooney, R. J. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3602-4_2 · Cited by 2
Inducing and refining rule-based knowledge from inexact examples
Leung, K., & Wong, M. (1991) · Knowledge Acquisition · doi:10.1016/1042-8143(91)90008-b · Cited by 2
Towards the automated debugging and maintenance of logic-based requirements models
McCluskey, T. L., & West, M. M. (2002) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.1998.732591 · Cited by 2
The Present and Future of Intelligent Tutoring Systems
Costa, E. J. F. (1992) · Springer eBooks · doi:10.1007/978-3-642-77750-9_8 · Cited by 2
Safe learning agents? Logic-based agents!
Alonso, E. (2002) · Cited by 1
SHAVLIK, J. W., & O’Rorke, P. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3602-4_7 · Cited by 1
Desiderata for generalization-to-N algorithms
Cohen, W. W. (1992) · Lecture notes in computer science · doi:10.1007/3-540-56004-1_10 · Cited by 1
“(Kedar-Cabelli and McCarty, 1987) Smadar Kedar-Cabelli and L. Thorne McCarty.”
MethodResultInfluentialAn extension of explanation-based generalization to negation as failure
Schrödl, S. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60343-3_26 · Cited by 1
Explanation-based generalization and constraint propagation with interval labels
Zercher, K. (1991) · Lecture notes in computer science · doi:10.1007/bfb0017023 · Cited by 1
“In ( Kedar-Cabelli & McCarty, 1987 ) a clear and concise EBG implementation is given.”
BackgroundExplanation-based generalization in game playing: Quantitative results
Schrödl, S. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026696 · Cited by 1
Learning Flexible Concepts Using a Two-Tiered Representation
Michalski, R. S., Bergadano, F., Matwin, S., & Zhang, J. (2007) · Springer eBooks · doi:10.1007/978-0-585-27366-2_5 · Cited by 1
Theory-based inductive learning: An integration of symbolic and quantitative methods
Science, C. (1988) · International Journal of Approximate Reasoning · doi:10.1016/0888-613x(88)90163-6 · Cited by 1
Explanation‐based learning as justification of knowledge
Wusteman, J. (1992) · Expert Systems · doi:10.1111/j.1468-0394.1992.tb00394.x · Cited by 1
Generalizing Explanation Structures
SHAVLIK, J. W., & Mooney, R. J. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3602-4_3 · Cited by 1
Simple low cost causal discovery using mutual information and domain knowledge
Joseph, A. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
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
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