Programming by Example Using Least General Generalizations
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1324836
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Articial Intelligence
- First page
- 283
- Volume
- 2014-July
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2014
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- By incorporating the least general generalization principle into the program synthesis algorithm, the method implicitly includes a ranking strategy and avoids expensive generation of all consistent programs. This dramatically increases the scope of program synthesis applications from simple transformations of text strings with 256 characters to editing richly formatted text documents represented as XML tree structures. This work was the first to apply the principle of inductive inference to programming-by-example (PBE) and raised expectations for program synthesis research, leading to a series of papers on PBE for complex tasks (by co-author M. Raza, Microsoft).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
