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Cited by 1,382AI-based Problem Solving and PlanningMachine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

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Explanation-Based Generalization: A Unifying View

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Mitchell, Tom M., Keller, Richard M., Kedar-Cabelli, Smadar T.·1986·Machine Learning
DOI 10.1023/a:1022691120807
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  • Inductive generalisation and predicate invention are not achieved in existing EBG paradigms, which assume a complete first-order domain theory.

    “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 Mitchell et al. 1986

    Section 4.2.1 says EBG assumes a domain theory sufficient to prove the training example belongs to the goal concept, so its generalizations follow deductively from existing knowledge. The EBG as Reformulating/Operationalizing/Deducing section likewise says the method compiles deductive consequences of its domain theory. These passages support the distinction from inductive generalisation, but they do not establish that EBG requires a complete first-order domain theory or address predicate invention. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 52

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Metadata

Authors
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T.
Affiliations
Rutgers, The State University of New Jersey (US)
ORCID
Mitchell, Tom M.
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1986-03
Volume
1
Issue
1
Pages
47-80
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1023/a:1022691120807
OpenAlex
W2180885055
Cited by
1,382
References
1
Citations by year
2026: 2 · 2025: 24 · 2024: 8 · 2023: 11 · 2022: 9 · 2021: 7 · 2020: 12 · 2019: 6 · 2018: 11 · 2017: 10 · 2016: 11 · 2015: 6 · 2014: 11 · 2013: 7 · 2012: 8
FWCI
104.74
Type
journal article
Language
en
Open access
Licence
Locations
Machine Learning (publishedVersion)
Funders
  • National Science Foundation (8351523, 8008889)
  • National Institutes of Health
  • Defense Advanced Research Projects Agency
  • Advanced Research Projects Agency
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
explanation-based generalization, concept learning, domain knowledge, explanation-based learning, unified framework, inductive generalization

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