Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach
This study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. The proposed system integrates the Human-in-the-Loop (HITL) paradigm, providing continuous feedback from huma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/12/259 |
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| author | Artem Isakov Danil Peregorodiev Ivan Tomilov Chuyang Ye Natalia Gusarova Aleksandra Vatian Alexander Boukhanovsky |
| author_facet | Artem Isakov Danil Peregorodiev Ivan Tomilov Chuyang Ye Natalia Gusarova Aleksandra Vatian Alexander Boukhanovsky |
| author_sort | Artem Isakov |
| collection | DOAJ |
| description | This study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. The proposed system integrates the Human-in-the-Loop (HITL) paradigm, providing continuous feedback from human evaluators, and it employs a sophisticated reward function to attenuate the effects of human-driven events. Novel mapping between reinforcement learning (RL) concepts and the Belief–Desire–Intention (BDI) framework is developed to enhance the explainability of the agent’s decision-making. A system is designed to adapt to changes in patient conditions and preferences while minimizing disruptions to existing schedules. Experimental results show a notable decrease in patient waiting times compared to conventional methods while adhering to operator-induced constraints. This approach offers a robust, explainable, and adaptable solution for the challenging tasks of scheduling in the environments that require human-centered decision-making. |
| format | Article |
| id | doaj-art-f9a603eb0e4e4ed1bad7afe7cd2bc88a |
| institution | OA Journals |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-f9a603eb0e4e4ed1bad7afe7cd2bc88a2025-08-20T02:01:24ZengMDPI AGTechnologies2227-70802024-12-01121225910.3390/technologies12120259Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent ApproachArtem Isakov0Danil Peregorodiev1Ivan Tomilov2Chuyang Ye3Natalia Gusarova4Aleksandra Vatian5Alexander Boukhanovsky6Department of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaDepartment of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaDepartment of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaDepartment of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaDepartment of Infocommunication Technologies, ITMO University, 197101 Saint Petersburg, RussiaThis study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. The proposed system integrates the Human-in-the-Loop (HITL) paradigm, providing continuous feedback from human evaluators, and it employs a sophisticated reward function to attenuate the effects of human-driven events. Novel mapping between reinforcement learning (RL) concepts and the Belief–Desire–Intention (BDI) framework is developed to enhance the explainability of the agent’s decision-making. A system is designed to adapt to changes in patient conditions and preferences while minimizing disruptions to existing schedules. Experimental results show a notable decrease in patient waiting times compared to conventional methods while adhering to operator-induced constraints. This approach offers a robust, explainable, and adaptable solution for the challenging tasks of scheduling in the environments that require human-centered decision-making.https://www.mdpi.com/2227-7080/12/12/259decision makingexplainabilitymulti-agent systemsreal timereinforcement learningscheduling |
| spellingShingle | Artem Isakov Danil Peregorodiev Ivan Tomilov Chuyang Ye Natalia Gusarova Aleksandra Vatian Alexander Boukhanovsky Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach Technologies decision making explainability multi-agent systems real time reinforcement learning scheduling |
| title | Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach |
| title_full | Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach |
| title_fullStr | Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach |
| title_full_unstemmed | Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach |
| title_short | Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach |
| title_sort | real time scheduling with independent evaluators explainable multi agent approach |
| topic | decision making explainability multi-agent systems real time reinforcement learning scheduling |
| url | https://www.mdpi.com/2227-7080/12/12/259 |
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