基于轮的超级个体-平衡速度和准确性

Pia Wilsdorf, M. Pierce, J. Hillston, A. Uhrmacher
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引用次数: 2

摘要

基于连续时间马尔可夫链语义的基于智能体或个体的模型在仿真领域受到越来越多的关注。为了降低计算成本,可以利用基于马尔可夫链集总的模型聚合技术。然而,对于具有嵌套的、有属性的代理和决定其动态的任意函数的模型,找到满足集块性条件的分区并不是一件容易的事。因此,我们利用了所谓的超级个体方法的潜力,其中代理的子种群由基于某些相似性标准的代表近似,并提出了一种基于轮的执行方案来平衡模拟的速度和准确性。为了实现这一目标,我们使用了一个富有表现力的基于规则的建模和仿真框架,使用鱼类栖息地模型评估性能,并讨论了未来研究的开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Round-based Super-Individuals - Balancing Speed and Accuracy
Agent- or individual-based models which are based on a continuous-time Markov chain semantics are increasingly receiving attention in simulation. To reduce computational cost, model aggregation techniques based on Markov chain lumping can be leveraged. However, for models with nested, attributed agents, and arbitrary functions determining their dynamics it is not trivial to find a partition that satisfies the lumpability conditions. Thus, we exploit the potential of the so-called super-individual approaches where sub-populations of agents are approximated by representatives based on some criteria for similarity, and propose a round-based execution scheme to balance speed and accuracy of the simulations. For realization we use an expressive rule-based modeling and simulation framework, evaluate the performance using a fish habitat model, and discuss open questions for future research.
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