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首页> 外文会议>Winter Simulation Conference >SIMULATION FRAMEWORK FOR SELF-EVOLVING AGENT-BASED MODELS: A CASE STUDY OF HOUSING MARKET MODEL
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SIMULATION FRAMEWORK FOR SELF-EVOLVING AGENT-BASED MODELS: A CASE STUDY OF HOUSING MARKET MODEL

机译：基于自演化Agent的模型的仿真框架：以住房市场模型为例

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Agent-based modeling and simulation (ABMS) has been applied to various domain problems. ABM consists of multiple agents and environments and focuses on their interactions. While such characteristics lead to its current popularity, they partially makes hard for the model calibration. Model calibration is generally performed by tuning model parameters, but in the ABM case, its interaction structure should be considered as well due to its huge influences to the results. To resolve this problem, this paper suggests a self-evolving ABMS framework. During the self-evolving process, ABM structure as well as parameters are explored and exploited for the model calibration. In particular, we adopt reconfigurable modeling in the proposed simulation framework, which is derived from Discrete Event System Specification (DEVS) formalism. This paper introduces a housing ABM, and the case study using this model shows that the accuracy of the model prediction was significantly increased through the self-evolving process.
机译：基于代理的建模和仿真（ABMS）已应用于各种领域问题。 ABM由多个代理和环境组成，并专注于它们的交互。尽管这些特性导致其当前流行，但它们在某种程度上使模型校准变得困难。通常通过调整模型参数来执行模型校准，但在ABM情况下，由于其对结果的巨大影响，因此也应考虑其相互作用结构。为了解决这个问题，本文提出了一个自我发展的ABMS框架。在自我演化过程中，将探索和利用ABM结构以及参数进行模型校准。特别是，我们在拟议的仿真框架中采用了可重配置的模型，该模型是从离散事件系统规范（DEVS）形式主义派生而来的。本文介绍了住房ABM，并使用此模型进行了案例研究，结果表明，通过自我演化过程，模型预测的准确性显着提高。

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