On Creating Complementary Pattern Databases
- Submitting institution
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Royal Holloway and Bedford New College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 35347549
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2017/601
- Title of conference / published proceedings
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- First page
- 4302
- Volume
- -
- Issue
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- ISSN
- 1045-0823
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Pattern databases (PDBs) are Planning heuristics based on lookup tables built from a simplified version of a problem. The proposed method iteratively builds complementary PDBs based on runtime predictions. This paper was the basis of three ICAPS-IPC 2018 optimal planners (Complementary1&2 and Planning-PDBs which were jointly awarded Runner-up, https://ipc2018-classical.bitbucket.io/#results). These planners are widely considered the current benchmark in non-portfolio optimal planning: multiple peer-reviewed papers (including 2 ICAPS 2019 papers, Seipp & Al Jair 2020, Fiser et al AAAI 2020, etc.) treat it as such.
- Author contribution statement
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- Non-English
- No
- English abstract
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