Human-machine collaborative optimization via apprenticeship scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this dom...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
AI Access Foundation
2020
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| Online Access: | https://hdl.handle.net/1721.1/125878 |