International Journal Papers

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Bae, J. W., & Moon, I. C. (2016). LDEF Formalism for Agent-Based Model Development. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 793–808.



Abstract : 

 As agent-based models (ABMs) are applied to various domains, the efficiency of model development has become an important issue in its applications. The current practice is that many models are developed from scratch, while they could have been built by reusing existing models. Moreover, when models need reconfiguration, they often need to be rebuilt significantly. These problems reduce the development efficiency and ultimately damage the efficacy of ABM. This paper partially resolves the challenges of model reusability from the systems engineering approach. Specifically, we propose a formalism-based ABM development and demonstrate its potential to promote model reuses. Our formalism, named large-scale, dynamic, extensible, and flexible (LDEF) formalism, encourages the building of a larger model by the composition of modularly developed components. Also, LDEF is tailored to the ABM contexts to represent the agent's action procedure and support the dynamic changes of their interactions. This paper shows that LDEF improves the model reusability in ABM development through its practical examples and theoretical discussions.



author={J. W. Bae and I. C. Moon}, 
journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, 
title={LDEF Formalism for Agent-Based Model Development}, 
keywords={Artificial intelligence;Biological system modeling;Context;Context modeling;Couplings;Agent-based model (ABM) formalism;efficient ABM development;formalism-based model development;model reusability}, 



Source Website :

No. Subject
32 Dongjun Kim, Kyungwoo Song, Yoon-Yeong Kim, Yongjin Shin, Wanmo Kang, Il-Chul Moon, Weonyoung Joo, Sequential Likelihood-Free Inference with Neural Proposal, Pattern Recognition Letters, Volume 169, 2023, Pages 102-109 file
31 Hyungho Na, Jaemyung Ahn, and Il-Chul Moon, "Weapon–Target Assignment by Reinforcement Learning with Pointer Network, " Journal of Aerospacce Information Systems, Vol. 20, No. 1 (2023), pp. 53-59 file
30 Tae-Sub Yun, Dongjun Kim, Il-Chul Moon, Jang Won Bae, (2022) Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect, Journal of Artificial Societies and Social Simulation file
29 Woo-Seop Yun, Sunggil Ko, Muhyun Byun, Heeyoung Kim, Il-Chul Moon and Tae-Eog Lee, Toward Robust Battle Experimental Design for Command and Control of Mechanized Infantry Brigade, Military Operations Research, Vol. 27, No. 1 (2022), pp. 45-72
28 J. H. Lee, I. -C. Moon and R. Oh, "Similarity Search on Wafer Bin Map Through Nonparametric and Hierarchical Clustering," in IEEE Transactions on Semiconductor Manufacturing, vol. 34, no. 4, pp. 464-474, Nov. 2021, doi: 10.1109/TSM.2021.3102679. file
27 Kim, D., Yun, TS., Moon, IC. et al. Automatic calibration of dynamic and heterogeneous parameters in agent-based models. Auton Agent Multi-Agent Syst 35, 46 (2021). file
26 Kim, Yoon-Yeong, et al. "Black-Box Expectation–Maximization Algorithm for Estimating Latent States of High-Speed Vehicles." Journal of Aerospace Information Systems 18.4 (2021): 175-192. file
25 Joo, W., Lee, W., Park, S., & Moon, I. C. (2020). Dirichlet variational autoencoder. Pattern Recognition, Vol. 107, 107514. file
24 Tae-Sub Yun and Il-Chul Moon, "Housing Market Agent-Based Simulation with Loan-To-Value and Debt-To-Income", Journal of Artificial Societies and Social Simulation, Vol. 23, Issue 4, 5, 2020 file
23 Jang, J.; Shin, S.; Lee, H.; Moon, I.-C. Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model. Sensors 2020, 20, 3845. file
22 J. W. Bae et al., "Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: A Case Study of Infantry Company Engagement," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 7, pp. 2551-2565, July 2020 file
21 Kim, Dohyung, Hyun-Shik Oh, and Il-Chul Moon. "Black-box Modeling for Aircraft Maneuver Control with Bayesian Optimization." International Journal of Control, Automation and Systems 17.6 (2019): 1558-1568. file
20 Shin, S. J., Kang, A., Kim, D., Lee, J., Bae, J. W., & Moon, I. C. (2019). Improving counterfire operations with enhanced command and control structure. Computational and Mathematical Organization Theory, 25(4), 464-498. file
19 J. W. Bae et al., "Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 9, pp. 1454-1469, Sept. 2018. file
18 Il-Chul Moon, Kyungwoo Song, Sang-Hyeon Kim, and Han-Lim Choi, State Prediction of High-speed Ballistic Vehicles with Gaussian Process, International Journal of Control, Automation and Systems, Vol. 16, Issue 3, June 2018, pp 1282-1292 file
17 Su-Jin Shin, Je-Yong Oh, Sungrae Park, Minki Kim, Il-Chul Moon, Hierarchical prescription pattern analysis with symptom labels, Pattern Recognition Letters, Volume 111, 2018, Pages 94-100 file
16 Sungrae Park, Doosup Choi, Minki Kim, Wonchul Cha, Chuhyun Kim, Il-Chul Moon, Identifying prescription patterns with a topic model of diseases and medications, Journal of Biomedical Informatics, Volume 75, November 2017, Pages 35-47 file
15 Su-Jin Shin, and Il-Chul Moon, "Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors", IEEE Transactions on Knowledge & Data Engineering. vol. 29 no. 2, p. 330-343, Feb., 2017 file
14 Bae, J. W., Bae, S. W., Moon, I. C., & Kim, T. G. (2016). Efficient Flattening Algorithm for Hierarchical and Dynamic Structure Discrete Event Models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 26(4), 25. file
» Bae, J. W., & Moon, I. C. (2016). LDEF Formalism for Agent-Based Model Development. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 793–808. file