A Meta-modeling Approach to Take into Account Data Domain Characteristics and Relationships in Information Visualizations

Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.

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Meta-Modeling Approach of Building CRM System Using GME-MOF

2007 International Conference on Convergence Information Technology (ICCIT 2007) ◽

10.1109/iccit.2007.4420489 ◽

2007 ◽

Author(s):

Subaji Mohan ◽

Eunmi Choi

Keyword(s):

Modeling Approach ◽

Crm System ◽

Meta Modeling

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A Meta-Modeling Approach for Capturing Recurrent Uses of Moodle Tools into Pedagogical Activities

Communications in Computer and Information Science - Computer Supported Education ◽

10.1007/978-3-319-29585-5_8 ◽

2016 ◽

pp. 132-151

Author(s):

Esteban Loiseau ◽

Nour El Mawas ◽

Pierre Laforcade

Keyword(s):

Modeling Approach ◽

Meta Modeling

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Mission Engineering and Design using Real-Time Strategy Games: An Explainable-AI Approach

Journal of Mechanical Design ◽

10.1115/1.4052841 ◽

2021 ◽

pp. 1-15

Author(s):

Adam Dachowicz ◽

Kshitij Mall ◽

Prajwal Balasubramani ◽

Apoorv Maheshwari ◽

Jitesh H. Panchal ◽

...

Keyword(s):

Real Time ◽

Initial Conditions ◽

Computational Design ◽

Mission Design ◽

Modeling Approach ◽

Strategy Games ◽

Explainable Ai ◽

Additional Data Collection ◽

Meta Modeling ◽

Rts Game

Abstract In this paper, we adapt computational design approaches, widely used by the engineering design community, to address the unique challenges associated with mission design using RTS games. Specifically, we present a modeling approach that combines experimental design techniques, meta-modeling using convolutional neural networks (CNNs), uncertainty quantification, and explainable AI (XAI). We illustrate the approach using an open-source real-time strategy (RTS) game called microRTS. The modeling approach consists of microRTS player agents (bots), design of experiments that arranges games between identical agents with asymmetric initial conditions, and an AI infused layer comprising CNNs, XAI, and uncertainty analysis through Monte Carlo Dropout Network analysis that allows analysis of game balance. A sample balanced game and corresponding predictions and SHapley Additive exPlanations (SHAP) are presented in this study. Three additional perturbations were introduced to this balanced gameplay and the observations about important features of the game using SHAP are presented. Our results show that this analysis can successfully predict probability of win for self-play microRTS games, as well as capture uncertainty in predictions that can be used to guide additional data collection to improve the model, or refine the game balance measure.

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