---
title: "Explanation-based learning: An alternative view (1986)"
description: "DeJong and Mooney argue that this framework misses important aspects of their own systems… 40 references, cited by 18 papers here."
authors: ["Gerald DeJong", "Raymond Mooney"]
year: 1986
venue: "Machine Learning"
source: "https://www.cs.utexas.edu/~ai-lab/pub-view.php?PubID=51403"
licence: "author's copy (UT Austin ML group) — read only"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dejong-1986-explanation-based-learning-alternative-view/"
---
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# Explanation-based learning: An alternative view

**Authors:** Gerald DeJong and [Raymond Mooney](https://md.research.elamur.ai/free/en/authors/raymond-j-mooney/) · **Year:** 1986 · **Venue:** [Machine Learning](https://md.research.elamur.ai/free/en/venues/machine-learning/) ([Machine Learning 1986](https://md.research.elamur.ai/free/en/venues/machine-learning/1986/)) · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** author's copy (UT Austin ML group) — read only

**Links:** [Publisher's page](https://doi.org/10.1007/bf00114116) · [PDF](https://www.cs.utexas.edu/~ml/papers/ebl-mlj-86.pdf) · [OpenAlex](https://openalex.org/W2101602574)

**Citations:** 950 citations (OpenAlex, 2026-10-14) · [18](https://md.research.elamur.ai/free/en/papers/dejong-1986-explanation-based-learning-alternative-view/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This 1986 paper from the Illinois explanation-based learning group responds to the unifying EBG framework of Mitchell, Keller, and Kedar-Cabelli. DeJong and Mooney argue that this framework misses important aspects of their own systems, notably their schema-acquisition work for natural language understanding, such as learning a kidnapping schema. They identify six weaknesses:
- The narrow focus on generalization rather than learning in general.
- Silence on how explanations are built, whether by internal problem solving or by observing others.
- A tendency to undergeneralize.
- A static, externally supplied operationality criterion.
- No role for hierarchically organized schemata.
- Reliance on reduced goal regression.

Framing the discussion in problem-solving terms, which they treat as equivalent to theorem proving, they propose an alternative generalization method. It supports retracting unifications so that explanations can be generalized further and altered, and they illustrate it on a kidnapping-style narrative. The paper matters because it widened the scope of explanation-based learning to include refinement, observation-driven learning, and schema reasoning. It also stresses that building large implementations across diverse domains exposes gaps in theory.

## Key points
- Published in Machine Learning 1 (1986), pp. 145–176, and supported by NSF grant NSF-IST-83-17899.
- The approach traces back to MACROPS in STRIPS. The paper credits its modern revival to LEX2 (Mitchell), LP (Silver), and ESA (DeJong).
- It recommends the term "Explanation-Based Learning" over EBG, partly because concept refinement could help with interacting conjunctive subgoals. The motivating example is a pail-like cup with a suitcase handle, which Winston's CUP domain overgeneralizes to accept.
- It argues the operationality criterion should be computed dynamically, because it only approximates how learning relates to the performance element.
- It contrasts explanation-based learning with ACT, SOAR, and EURISKO. Existing explanation-based systems mainly perform "schema composition" of known operators.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 228 works · 74,590 citations

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,948 works

## Cite

Cite as: Gerald DeJong and Raymond Mooney. 1986. Explanation-based learning: An alternative view. Machine Learning.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dejong1986explanation,
  title = {{Explanation-based learning: An alternative view}},
  author = {Gerald DeJong and Raymond Mooney},
  booktitle = {Machine Learning},
  year = {1986},
  doi = {10.1007/bf00114116},
  url = {https://doi.org/10.1007/bf00114116}
}
```

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## References

40 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Anderson, J.R. (1983). Acquisition of proof skills in geometry. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 191-221). Palo Alto, CA: Tioga. [find](https://scholar.google.com/scholar?q=Anderson%2C+J%2ER%2E+%281983%29%2E+Acquisition+of+proof+skills+in+geometry%2E+In+R%2ES%2E+Michalski%2C+J%2EG%2E+Carbonell%2C+%26+T%2EM%2E+Mitchell+%28Eds%2E%29%2C+Machine+learning%3A+An+artificial+intelligence+approach+%28pp%2E+191%2D221%29%2E+Palo+Alt)
2. Carbonell, J.G., Michalski, R.S., & Mitchell, T.M. (1983). An overview of machine learning. In R.S. Michalski, J.G. Carbonell, & T.M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 331-363). Palo Alto, CA: Tioga. [find](https://scholar.google.com/scholar?q=Carbonell%2C+J%2EG%2E%2C+Michalski%2C+R%2ES%2E%2C+%26+Mitchell%2C+T%2EM%2E+%281983%29%2E+An+overview+of+machine+learning%2E+In+R%2ES%2E+Michalski%2C+J%2EG%2E+Carbonell%2C+%26+T%2EM%2E+Mitchell+%28Eds%2E%29%2C+Machine+learning%3A+An+artificial+intelligence+appr)
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*Updated 3 November 2026*