• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Proceedings of the IASTED international conferences on informatics >MULTI-STAGE, HIGH PERFORMANCE, SELF-OPTIMISING DOMAIN-SPECIFIC LANGUAGE FOR SPATIAL AGENT-BASED MODELS
【24h】

MULTI-STAGE, HIGH PERFORMANCE, SELF-OPTIMISING DOMAIN-SPECIFIC LANGUAGE FOR SPATIAL AGENT-BASED MODELS

机译：基于空间代理的模型的多阶段，高性能，自优化域特定语言

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Optimisation in the context of Agent-based Modelling has been thoroughly researched and reported in the literature. In particular, model parameter tuning has been done using a variety of parametric optimisers, and we are now entering a phase where agent behaviour itself is learned, not specified. The latter is proving to be problematic for a number of reasons. Algorithms earmarked for this purpose such as Genetic Programming and decision tree induction present their own problems. Defining the search space in terms of building blocks for these algorithms is surprisingly difficult. We propose a different methodology for accomplishing machine learning in the context of model induction. Instead of forcing the modeller to provide fine grained and concise model building blocks, we provide a language where small portions of uncertain dynamics can be expressed concisely using domain specific knowledge. This has the potential to greatly increase the efficiency of building simulations for models, and reduce time spent on verification. Our language is built using recent concepts of multi-stage programming (MSP), providing run-time compiling and execution of code. This allows us to avoid the ion penalty. We provide detailed examples, and performance data for our implementation.
机译：在基于Agent的建模环境中的优化已经得到了充分的研究，并在文献中进行了报道。特别是，已经使用各种参数优化器完成了模型参数的调整，并且我们现在进入一个阶段，在此阶段中，学习代理行为本身（未指定）。事实证明，后者有许多原因。为此目的而指定的算法，例如遗传编程和决策树归纳，都存在自己的问题。用这些算法的构建块来定义搜索空间非常困难。我们提出了一种在模型归纳的背景下完成机器学习的不同方法。我们没有强迫建模者提供细粒度和简洁的模型构建块，而是提供了一种语言，其中可以使用特定于领域的知识来简洁地表达不确定动态的小部分。这有可能极大地提高建立模型仿真的效率，并减少验证所花费的时间。我们的语言是使用最新的多阶段编程（MSP）概念构建的，可提供运行时编译和代码执行。这使我们避免了离子损失。我们提供了详细的示例以及用于实施的性能数据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号