Selected Publications


Il-Chul Moon and Kathleen M. Carley, Self-Organizing Social and Spatial Networks under What-if Scenarios, Autonomous Agent and Multi-Agent Systems (AAMAS’07), Honolulu, Hawaii, May 14-18, 2007, pp 127-134

 

Abstract :

 Multi-agent models have been used to simulate complex systems in many domains. In some models, the agents move in a physical/grid space and are constrained by their locations on the spatial space, e.g. Sugarscape. In others, the agents interact in a social multi-dimensional space and are bound to their knowledge and social positions, e.g. Construct. However, many real world problems require a mixed model containing both spatial and social features. This paper introduces such a multi agent system, Construct-Spatial, which simulates agent communication and movement simultaneously. It is an extended version of Construct, which is a multi-agent social model, and its extension is based on a multi-agent grid model, Sugarscape. To understand the impact of this integration of the two spaces, we run virtual experiments and compare the output from the combined space to those from each of the two spaces. The initial analysis reveals that the integration facilitates unbalanced knowledge distribution across the agents compared to the grid-only model and limits agent network connections compared to the social network model without spatial constraints. After the comparisons, we setup what-if scenarios where we varied the type of the threats faced by network and observe their emergent behaviors. From the what-if analyses, we locate the best destabilization scenario and find the propagation of the effects from the spatial space to the social network space. We believe that this model can be a conceptual model for assessing the efficiency and the robustness of team deployments, network node distributions, sensor distributions, etc.

 

@inproceedings{Moon:2007:SSS:1329125.1329430,
 author = {Moon, Il-Chul and Carley, Kathleen M.},
 title = {Self-organizing Social and Spatial Networks Under What-if Scenarios},
 booktitle = {Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems},
 series = {AAMAS '07},
 year = {2007},
 isbn = {978-81-904262-7-5},
 location = {Honolulu, Hawaii},
 pages = {252:1--252:8},
 articleno = {252},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/1329125.1329430},
 doi = {10.1145/1329125.1329430},
 acmid = {1329430},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {multi-agent system, network evolution, organizational structure},
} 

 

Source Website : 

 http://dl.acm.org/citation.cfm?id=1329430

No. Subject
36 DongHyeok Shin, Seungjae Shin, Il-Chul Moon, Frequency Domain-based Dataset Distillation, Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA, Dec 10-Dec 16, 2023 file
35 Suhyeon Jo, Donghyeok Shin, Byeonghu Na, JoonHo Jang, and Il-Chul Moon, Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy, ACM International Conference on Information and Knowledge Management (CIKM 2023) file
34 Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, SAAL: Sharpness-Aware Active Learning, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
33 Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon, Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models, International Conference on Machine Learning (ICML 2023), Hawaii, USA, Jul 25-27, 2023 file
32 Seungjae Shin, Heesun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon, Loss Curvature Matching for Dataset Selection and Condensation, International Conference on Artificial Intelligence and Statistics (AISTATS 23), Valencia, Spain, Apr 25-27, 2023 file
31 Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon, Maximum Likelihood Training of Implicit Nonlinear Diffusion Model, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
30 JoonHo Jang, Byeonghu Na, Dong Hyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon, Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation, Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, Nov 28-Dec 9, 2022 file
29 Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon, Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation, International Conference on Machine Learning (ICML 2022) file
28 HeeSun Bae, Seungjae Shin, Byeonghu Na, JoonHo Jang, Kyungwoo Song, Il-Chul Moon, From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model, International Conference on Machine Learning (ICML 2022) file
27 Yooon-Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon, LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning, Neural Information Processing Systems (NeurIPS 2021), Virtual, Dec. 7-10, 2021 file
26 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
25 Mingi Ji, Seungjae Shin, Seunghyun Hwang, Gibeom Park, Il-Chul Moon. Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation. Conference on Computer Vision and Pattern Recognition (CVPR 2021). file
24 Hyemi Kim, Seungjae Shin, JoonHo Jang, Kyungwoo Song, Weonyoung Joo, Wanmo Kang, and Il-Chul Moon, Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder, AAAI Conference on Artificial Intelligence (AAAI 2021) file
23 Kyungwoo Song, Yohan Jung, Dongjun Kim, and Il-Chul Moon, Implicit Kernel Attention, AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, Feb. 2-9 file
22 Byeonghu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoon-Yeong Kim, and Il-Chul Moon, Deep Generative Positive-Unlabeled Learning under Selection Bias, ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, Oct. 19 file
21 Joo, W., Lee, W., Park, S., & Moon, I. C. (2020). Dirichlet variational autoencoder. Pattern Recognition, Vol. 107, 107514. file
20 Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, and Il-Chul Moon, Sequential Recommendation with Relation-Aware Kernelized Self-Attention​. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
19 Su-Jin Shin, Kyungwoo Song, and Il-Chul Moon, Hierarchically Clustered Representation Learning, AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
18 Kyungwoo Song, JoonHo Jang, Seung jae Shin and Il-Chul Moon, Bivariate Beta-LSTM. AAAI Conference on Artificial Intelligence (AAAI 2020). New York. Feb. 7-12 file
17 Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, and Il-Chul Moon, Adversarial Dropout for Recurrent Neural Networks. AAAI Conference on Artificial Intelligence (AAAI 2019). Hawaii. Jan. 27-Feb. 1 file