---
title: "Selectively Generalizing Plans for Problem-Solving — Minton 1985"
description: "Minton presents MORRIS, a best-first heuristic problem solver in the STRIPS tradition that keeps only… 9 references, cited by 6 papers here."
authors: ["Steven Minton"]
year: 1985
venue: "IJCAI 1985"
source: "https://mlanthology.org/ijcai/1985/minton1985ijcai-selectively/"
licence: "All rights reserved — free to read at the source"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/minton-1985-selectively-generalizing-plans-problem-solving/"
---
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# Selectively Generalizing Plans for Problem-Solving

**Authors:** [Steven Minton](https://md.research.elamur.ai/free/en/authors/steven-minton/) · **Year:** 1985 · **Venue:** [IJCAI 1985](https://md.research.elamur.ai/free/en/venues/international-joint-conference-on-artificial-intelligence/) ([International Joint Conference on Artificial Intelligence 1985](https://md.research.elamur.ai/free/en/venues/international-joint-conference-on-artificial-intelligence/1985/)) · **Licence:** All rights reserved — free to read at the source

**Links:** [Publisher's page](https://mlanthology.org/ijcai/1985/minton1985ijcai-selectively/) · [PDF](https://ijcai.org/Proceedings/85-1/Papers/114.pdf) · [OpenAlex](https://openalex.org/W1747974990)

**Citations:** 154 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/minton-1985-selectively-generalizing-plans-problem-solving/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Systems that store generalized plans (macro-operators), as STRIPS did with Macrops, risk being overwhelmed by their own experience. As stored macros multiply, matching and choosing among them can cost as much as searching the original space, and the ordering bias that made them useful fades. Minton presents MORRIS, a best-first heuristic problem solver in the STRIPS tradition that keeps only plan fragments judged worth their cost. It retains two kinds. S-macros ("scripts") are short subsequences that recur across solutions; their number is capped, and the least-used are dropped. T-macros ("tricks") are sequences that looked unpromising under the heuristic H_diff but still led to success, including goal reorderings forced by interacting goals. In a 26-operator robot world, MORRIS was compared with MAX, a STRIPS-style learner that saves every usable macro, and with a non-learning baseline. MORRIS evaluated fewer branches than MAX and generally beat the non-learner. MAX was usually slower than the baseline whenever the baseline could solve a problem. The work frames "what to learn" as a central question for learning problem solvers.

## Key points
- MORRIS learns selectively using two utility criteria: frequency of use (s-macros) and usefulness on hard, heuristically misleading subproblems (t-macros, which help escape local maxima of H_diff).
- The comparison system MAX copies STRIPS's practice of generalizing whole solutions and treating every composable subsequence as a potential macro. Its search procedure is otherwise identical to MORRIS's.
- The test domain is a robot world with 26 operators and 5 rooms. It includes doors, boxes, lights, tools and food, and many of its problems involve interacting goals. The evaluation used a series of 25 problems ordered from easier to harder, with a 100 CPU-second cutoff.
- T-macros gave the larger gains. S-macros helped more often but by smaller margins, and their applicability tests sometimes made MORRIS slower than the non-learner.
- The reported timings cover search only, not macro generalization. The author notes that learning time was typically much smaller than search time.

*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

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 633 works · 195,217 citations

📚 Shelves (3): [🖥️ 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 · [🌐 Computer Networks and Communications](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-networks-and-communications/) · 115 works

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

## Cite

Cite as: Steven Minton. 1985. Selectively Generalizing Plans for Problem-Solving. IJCAI 1985.

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

```bibtex
@inproceedings{minton1985selectively,
  title = {{Selectively Generalizing Plans for Problem-Solving}},
  author = {Steven Minton},
  booktitle = {IJCAI 1985},
  year = {1985},
  url = {https://mlanthology.org/ijcai/1985/minton1985ijcai-selectively/}
}
```

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- [↑ back to papers of 1985](https://md.research.elamur.ai/free/en/papers/1985/)

## References

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

1. Ernst, G. and Newell, A.. GPS: A Case Study in Generality and Problem Solving. Academic Press, 1969. [find](https://scholar.google.com/scholar?q=Ernst%2C+G%2E+and+Newell%2C+A%2E%2E+GPS%3A+A+Case+Study+in+Generality+and+Problem+Solving%2E+Academic+Press%2C+1969%2E)
2. Pikes, R. Monitored execution of Robot Plans produced by STRIPS. Proceedings IFIP Congress, 1971. [find](https://scholar.google.com/scholar?q=Pikes%2C+R%2E+Monitored+execution+of+Robot+Plans+produced+by+STRIPS%2E+Proceedings+IFIP+Congress%2C+1971%2E)
3. Fikes, R., Hart, P. and Nilsson, N. "[Learning and Executing Generalized Robot Plans](https://md.research.elamur.ai/free/en/papers/fikes-1972-learning-executing-generalized-robot-plans/)." Artificial Intelligence 3,4(1972).
4. Fikes, R. and Nilsson, N. "[STRIPS: A new approach to the application of theorem proving to problem solving](https://md.research.elamur.ai/free/en/papers/fikes-1971-strips-new-approach-application-theorem/)." Artificial Intelligence 2 (1971).
5. Korf, Richard E. Operator Decomposabllity: A New Type of Problem Structure. Proceedings AAAI-83,1983. [find](https://scholar.google.com/scholar?q=Korf%2C+Richard+E%2E+Operator+Decomposabllity%3A+A+New+Type+of+Problem+Structure%2E+Proceedings+AAAI%2D83%2C1983%2E)
6. Minton, S. Constraint-Based Generalization. AAAI Proceedings, 1964. [find](https://scholar.google.com/scholar?q=Minton%2C+S%2E+Constraint%2DBased+Generalization%2E+AAAI+Proceedings%2C+1964%2E)
7. Mitchell, T., Utgoff, P. and Banerjl, R. Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics. In Machine Learning, Carbonell, J., Michalski, R. and Mitchell, T., Ed.Tioga Publishing Co., 1983. [find](https://scholar.google.com/scholar?q=Mitchell%2C+T%2E%2C+Utgoff%2C+P%2E+and+Banerjl%2C+R%2E+Learning+by+Experimentation%3A+Acquiring+and+Refining+Problem%2DSolving+Heuristics%2E+In+Machine+Learning%2C+Carbonell%2C+J%2E%2C+Michalski%2C+R%2E+and+Mitchell%2C+T%2E%2C+Ed%2ETioga+Pu)
8. O'Rorke, Paul. Generalization for Explanation-based Schema Acquisition. Proceedings AAAI, 1984. [find](https://scholar.google.com/scholar?q=O%27Rorke%2C+Paul%2E+Generalization+for+Explanation%2Dbased+Schema+Acquisition%2E+Proceedings+AAAI%2C+1984%2E)
9. Porter, B. and Kibler, D. Learning Operator Transformations, AAAI Proceedings, 1984. 1 0 . Sacerdoti, E. "Planning in a Hierarchy of Abstraction Spaces." Artificial Intelligence 5 (1974). [find](https://scholar.google.com/scholar?q=Porter%2C+B%2E+and+Kibler%2C+D%2E+Learning+Operator+Transformations%2C+AAAI+Proceedings%2C+1984%2E+1+0+%2E+Sacerdoti%2C+E%2E+%22Planning+in+a+Hierarchy+of+Abstraction+Spaces%2E%22+Artificial+Intelligence+5+%281974%29%2E)

*Updated 3 November 2026*