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- W4312699559 abstract "To improve the autonomy of Cyber-Physical Production Systems (CPPS), a growing number of approaches in Artificial Intelligence (AI) is developed. However, implementations of such approaches are often validated on individual use-cases, offering little to no comparability. Though CPPS automation includes a variety of problem domains, existing benchmarks usually focus on single or partial problems. Additionally, they often neglect to test for AI-specific performance indicators, like asymptotic complexity scenarios or runtimes. Within this paper we identify minimum common set requirements for AI benchmarks in the domain of CPPS and introduce a comprehensive benchmark, offering applicability on diagnosis, reconfiguration, and planning approaches from AI. The benchmark consists of a grid of datasets derived from 16 simulations of modular CPPS from process engineering, featuring multiple functionalities, complexities, and individual and superposed faults. We evaluate the benchmark on state-of-the-art AI approaches in diagnosis, reconfiguration, and planning. The benchmark is made publicly available on GitHub." @default.
- W4312699559 created "2023-01-05" @default.
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- W4312699559 date "2022-09-06" @default.
- W4312699559 modified "2023-10-17" @default.
- W4312699559 title "An AI benchmark for Diagnosis, Reconfiguration & Planning" @default.
- W4312699559 doi "https://doi.org/10.1109/etfa52439.2022.9921546" @default.
- W4312699559 hasPublicationYear "2022" @default.
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