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A Context-Aware Decision Support Framework for Scientific Experiment Configuration
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DOI：10.1002/spe.70085.png)
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Abstract
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Defining an experimental configuration is a complex decision problem for early-stage researchers, who must map goals, constraints, and requirements onto datasets, algorithms, and parameter settings that directly affect experimental outcomes. Existing scientific workflow engines improve execution and reproducibility; however, they rarely capture the decision rationale behind configuration choices, which is needed to inform future selections.
Keywords:
contextualisation
decision-making
human–AI interaction
Markov decision process (MDP)
scientific experiment
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