BROWSE
Detail
Conference Paper
Incremental Self-Evolving Framework for Agent-Based Simulation
Cited 2 time in 
Share 
Share
- Authors
- Dong-oh Kang, Jang Won Bae, Euihyun Paik
- Issue Date
- 2016-12
- Citation
- International Conference on Computational Science and Computational Intelligence (CSCI) 2016, pp.1428-1429
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper presents a self-evolving scheme of an agent-based simulation to improve agent simulation models to fit real world data in an incremental way without human intervention by machine learning techniques. The proposed method can solve problems of traditional optimization methods of the agent-based simulation by the incremental optimization of agent simulation models. In this paper, we introduce requirements, the structure and internal functions of the self-evolving agent-based simulation framework for social simulation.
- KSP Keywords
- Agent simulation, Machine Learning technique(MLT), Optimization methods, Real-world, Simulation Model, Simulation framework, Social Simulation, agent-based simulation, human intervention, self-evolving