Data mining for hierarchical model creation
Data mining for hierarchical model creation
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DOI：
10.1109/tsmcc.2007.897341
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发表时间：
2007-07-01
期刊：
影响因子：
--
通讯作者：
Cook, Diane J.
中科院分区：
文献类型：
--
作者：
Youngblood, G. Michael;Cook, Diane J.
本文研究了智能环境中居民行为模型的学习问题。我们认为，居民之间的互动是智能的。可以使用数据驱动的方法自动化环境，以生成分层居民模型并学习决策策略。为了验证这一假设，我们设计了ProPHeT决策学习算法，该算法基于传感器观察、电力线控制和生成的分层模型来学习控制智能环境的策略。利用从MavHome智能家居和智能办公环境中收集的真实数据对算法的性能进行了评估。
In this paper, we examine the problem of learning inhabitant behavioral models in intelligent environments. We maintain that inhabitant interactions in smart. environments can be automated using a data-driven approach to generate hierarchical inhabitant models and learn decision policies. To validate this hypothesis, we have designed the ProPHeT decision-learning algorithm that learns a strategy for controlling a smart environment based on sensor observation, power line control, and the generated hierarchical model. The performance of the algorithm is evaluated using real data collected from our MavHome smart home and smart office environments.