20.500.12556/RUL-183995 A context-aware decision support framework for scientific experiment configuration Introduction: 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. Method: We propose a context-aware decision-support framework that formalises experiment configuration as a structured and sequential decision problem. The framework combines three components: a semantic Knowledge Graph (KG) storing historical configurations, contextual attributes, and decision rationale; an MDP-based Option Explorer that filters the KG under user-defined constraints and ranks feasible configurations by expected cumulative reward; and a Graphical User Interface for specifying constraints, inspecting ranked alternatives, and providing structured feedback. Unlike existing workflow systems, the framework explicitly separates user-defined context from automated reasoning, producing an interpretable ranked list rather than a single opaque recommendation. We evaluated the framework in a user study with 90 MSc- and PhD-level researchers performing a model-selection task, using a synthetic dataset of one million experimental configurations under three levels of contextual detail. Results: Compared with manual search, the framework reduced decision time (up to 68%), reduced perceived difficulty (up to 36%), and increased user satisfaction (up to 43%) under the constrained condition. Conclusion: By formalising the link between experimental context and probabilistic decision ranking, the framework improves reproducibility and scalability of decision support in scientific experimentation. contextualisation decision-making human–AI interaction Markov decision process MDP scientific experiment kontekstualizacija odločanje interakcija med človekom in umetno inteligenco Markovski odločitveni proces znanstveni eksperiment true false true Angleški jezik Slovenski jezik Članek v reviji 2026-06-23 14:43:50 2026-06-23 14:43:51 2026-06-24 04:17:22 0000-00-00 00:00:00 2026 0 0 Str. 1-20 no. Vol. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8 0038-0644 10.1002/spe.70085 279799811 RAZ_Miri_Pouriya_2026.pdf RAZ_Miri_Pouriya_2026.pdf 1 7EF0754FEBE0353AB79002B219BD58A1 27b9e650c8a1bd5704a470eefb319bd2d5a3adddcd273773f803e9f8e16064a3 8051f920-6f01-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236375 https://onlinelibrary.wiley.com/doi/10.1002/spe.70085 1 1bd591e1-6f01-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=236373 Fakulteta za računalništvo in informatiko 0 0 0