Using Classification Learning in Companion Modeling
Using Classification Learning in Companion Modeling
复制标题
在同伴建模中使用分类学习
DOI：
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复制
发表时间：
2009
期刊：
影响因子：
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通讯作者：
C. Vejpas
中科院分区：
文献类型：
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作者：
D. Torii;François Bousquet;T. Ishida;G. Trébuil;C. Vejpas
伴随建模是一种用于促进用户对可再生资源进行适应性管理的方法。它正在使用角色扮演游戏（RPG）和多智能体模拟来验证代表要管理的复杂系统功能的初始模型。在这项研究中，我们提出了一种新的代理模型的构建方法，其中分类学习应用于RPG日志数据的同伴建模。这种方法使得能够进行系统的模型构建，该模型构建处理多参数，独立于建模者的能力。将分类学习应用于RPG日志数据存在三个问题：1）由于收集数据的成本很高，很难收集足够多的特征数据。2)噪声数据可能会影响学习结果，因为数据量可能不足。3)学习结果应被解释为人类决策模型，并应被专家认为反映了现实。我们使用以下两种方法实现了智能体模型构建系统：1）使用特征选择方法，识别出具有最佳预测精度的特征子集。在这个过程中，专家选择的重要特征总是包括在内。2)专家在通过结果的可视化评估学习模型之后从学习结果中消除不相关的特征。最后，使用的RPG日志数据从同伴建模的案例研究，在泰国东北部的水稻生产，我们证实了这种方法的能力。
Companion Modeling is a methodology used to facilitate adaptive management of renewable resources by their users. It is using role-playing games (RPG) and multiagent simulations to validate initial models representing the functioning of complex systems to be managed. In this research, we propose a novel agent model construction methodology in which classification learning is applied to the RPG log data in Companion Modeling. This methodology enables a systematic model construction that handles multi-parameters, independent of the modelers' ability. There are three problems in applying classification learning to the RPG log data: 1) It is difficult to gather enough data for the number of features because the cost of gathering data is high. 2) Noise data can affect the learning results because the amount of data may be insufficient. 3) The learning results should be explained as a human decision making model and should be recognized by the expert as reflecting reality. We realized an agent model construction system using the following two approaches: 1) Using a feature selection method, the feature subset that has the best prediction accuracy is identified. In this process, the important features chosen by the expert are always included. 2) The expert eliminates irrelevant features from the learning results after evaluating the learning model through a visualization of the results. Finally, using the RPG log data from a Companion Modeling case study on rice production in northeastern Thailand, we confirm the capability of this methodology.
DOI：
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发表时间：
1999
期刊：
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影响因子：
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作者：
Nigel Gi lbert;K. G. Troitzsch
通讯作者：
Nigel Gi lbert;K. G. Troitzsch