A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-3344
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2016.07.004
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 103
- Volume
- 241
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- 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)
-
B - AI (Artificial Intelligence)
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The PEORL framework https://doi.org/10.24963/ijcai.2018/675 and its later developments (for instance https://doi.org/10.1609/aaai.v33i01.33012970) have been based on a hierarchical extension of DARLING, and evaluated on one of the domains introduced in this article. The combination of planning and learning introduced here was further developed into a PhD project, which led to the concept being extended to motion planning for robot manipulation https://doi.org/10.1109/IROS40897.2019.8967717.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
