Mean Payoff Optimization for Systems of Periodic Service and Maintenance
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Proceedings |
| Conference | Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, |
| MU Faculty or unit | |
| Citation | |
| web | Paper URL |
| Doi | http://dx.doi.org/10.24963/ijcai.2023/598 |
| Keywords | Periodic Maintenance; strategy synthesis |
| Attached files | |
| Description | Consider oriented graph nodes requiring periodic visits by a service agent. The agent moves among the nodes and receives a payoff for each completed service task, depending on the time elapsed since the previous visit to a node. We consider the problem of finding a suitable schedule for the agent to maximize its long-run average payoff per time unit. We show that the problem of constructing an epsilon-optimal schedule is PSPACE-hard for every fixed non-negative epsilon, and that there exists an optimal periodic schedule of exponential length. We propose randomized finite-memory (RFM) schedules as a compact description of the agent's strategies and design an efficient algorithm for constructing RFM schedules. Furthermore, we construct deterministic periodic schedules by sampling from RFM schedules. |
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