An alternative to the black box: Strategy learning.
In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delay...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0264485 |
| _version_ | 1811229233346772992 |
|---|---|
| author | Simon Traub Oleg S Pianykh |
| author_facet | Simon Traub Oleg S Pianykh |
| author_sort | Simon Traub |
| collection | DOAJ |
| description | In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are "black box" algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limiting their practical use. In this work, we propose an alternative approach: using machine learning to generate optimal, comprehensible strategies which can be understood and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain near-optimal results. |
| first_indexed | 2024-04-12T10:11:06Z |
| format | Article |
| id | doaj.art-676cb658435f406bb053b1e5bf41b88f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T10:11:06Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-676cb658435f406bb053b1e5bf41b88f2022-12-22T03:37:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01173e026448510.1371/journal.pone.0264485An alternative to the black box: Strategy learning.Simon TraubOleg S PianykhIn virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are "black box" algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limiting their practical use. In this work, we propose an alternative approach: using machine learning to generate optimal, comprehensible strategies which can be understood and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain near-optimal results.https://doi.org/10.1371/journal.pone.0264485 |
| spellingShingle | Simon Traub Oleg S Pianykh An alternative to the black box: Strategy learning. PLoS ONE |
| title | An alternative to the black box: Strategy learning. |
| title_full | An alternative to the black box: Strategy learning. |
| title_fullStr | An alternative to the black box: Strategy learning. |
| title_full_unstemmed | An alternative to the black box: Strategy learning. |
| title_short | An alternative to the black box: Strategy learning. |
| title_sort | alternative to the black box strategy learning |
| url | https://doi.org/10.1371/journal.pone.0264485 |
| work_keys_str_mv | AT simontraub analternativetotheblackboxstrategylearning AT olegspianykh analternativetotheblackboxstrategylearning AT simontraub alternativetotheblackboxstrategylearning AT olegspianykh alternativetotheblackboxstrategylearning |