Learning Rule-Based Explanatory Models from Exploratory Multi-Simulation for Decision-Support Under Uncertainty

• Published in: Proceedings - Winter Simulation Conference pp. 2293 - 2304
• Main Authors: Rodriguez, Brodderick, Yilmaz, Levent
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.12.2020
• Subjects: Computational modeling; Context modeling; Data models; Decision making; Maintenance engineering; Uncertainty
• ISSN: 1558-4305
• DOI: 10.1109/WSC48552.2020.9383858

Exploratory modeling and simulation is an effective strategy when there are substantial contextual uncertainty and representational ambiguity in problem formulation. However, two significant challenges impede the use of an ensemble of models in exploratory simulation. The first challenge involves streamlining the maintenance and synthesis of multiple models from plausible features that are identified from and subject to the constraints of the research hypothesis. The second challenge is making sense of the data generated by multi-simulation over a model ensemble. To address both challenges, we introduce a computational framework that integrates feature-driven variability management with an anticipatory learning classifier system to generate explanatory rules from multi-simulation data.