Computing Convex Coverage Sets for Faster Multi-objective Coordination
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1969
- Type
- D - Journal article
- DOI
-
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
- -
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- 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
-
2
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper investigates the topic of multi-objective coordination: how multiple cooperating agents can coordinate their behaviour to balance multiple competing objectives. It presents two novel algorithms for doing so and analyses them theoretically and empirically, demonstrating far better scalability in the number of agents than had previously been achieved. It also shows how to extend known algorithms from the single-objective case to multi-objective, yielding algorithms that achieve much better scalability in the number of agents than the state of the art.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
