A context-aware decision support framework for scientific experiment configuration

Miri, Pouriya (avtor), Stankovski, Vlado (avtor), Veljković, Kristina (avtor), Kochovski, Petar (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: contextualisation, decision-making, human–AI interaction, Markov decision process, MDP, scientific experiment
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: Str. 1-20
• Številčenje: Vol. , no.
• PID: 20.500.12556/RUL-183995
• UDK: 004.8
• ISSN pri članku: 0038-0644
• DOI: 10.1002/spe.70085
• COBISS.SI-ID: 279799811
• Datum objave v RUL: 23.06.2026

Gradivo je del revije

• Naslov: Software : practice & experience
• Skrajšan naslov: Softw. pract. exp.
• Založnik: Wiley
• ISSN: 0038-0644
• COBISS.SI-ID: 5222919

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: kontekstualizacija, odločanje, interakcija med človekom in umetno inteligenco, Markovski odločitveni proces, znanstveni eksperiment