Computing Convex Coverage Sets for Faster Multi-objective Coordination
- Submitting institution
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The University of Liverpool
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11988
- Type
- D - Journal article
- DOI
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10.1613/jair.4550
- Title of journal
- Journal of Artificial Intelligence Research
- Article number
- -
- First page
- 399
- Volume
- 52
- Issue
- -
- ISSN
- 1076-9757
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- 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)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper combines and extends "Computing convex coverage sets for multi-objective coordination graphs" (ADT'13) and "Linear support for multi-objective coordination graphs" (AAMAS'14). The two new algorithms from the paper, MOVE and OLS, have been used and extended many times. For example, MOVE was used by Bargiacchi et al (ICML'18) and is included in the AI-Toolbox (https://github.com/Svalorzen/AI-Toolbox, JMLR'20). Extensions of OLS include Approximate OLS (AOLS), OLS with Alpha-matrix Reuse (OLSAR), Deep Optimistic Linear Support Learning (DOL), and variational optimistic linear support (VOLS), which appeared at ICAPS, IJCAI and NeurIPS workshops.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -